Acknowledgements	This work was supported by Contracts HR0011-15-C-0113 and HR0011-18-2-0052 with the US Defense Advanced Research Projects Agency (DARPA). Approved for Public Release, Distribution Unlimited. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.


Introduction	Domain adaptation is a machine learning paradigm that aims at improving the generalization performance of a new (target) domain by using a dataset from the original (source) domain. Suppose that, as the source domain dataset, we have a captioning corpus, consisting of images of daily lives and each image has captions. Suppose also that we would like to generate captions for exotic cuisine, which are rare in the corpus. It is usually very costly to make a new corpus for the target domain, i.e., taking and captioning those images. The research question here is how we can leverage the source domain dataset to improve the performance on the target domain. As described by Daumé daume:07, there are mainly two settings of domain adaptation: fully supervised and semi-supervised. Our focus is the supervised setting, where both of the source and target domain datasets are labeled. We would like to use the label information of the source domain to improve the performance on the target domain. Recently, Recurrent Neural Networks (RNNs) have been successfully applied to various tasks in the field of natural language processing (NLP), including language modeling  , caption generation  and parsing  .


System Design	In this section, we describe the system design, and workflow of SimplerVoice (Figure FIGREF1 ). SimplerVoice has 4 main components: input retrieval, object2text, text2visual, and output display. Figure FIGREF1 provides the overall structure of SimplerVoice system.


Analysis on the Cache-based Neural Model	As shown above, the topic cache is able to influence both the translations of beginning sentences and those of subsequent sentences while the dynamic cache built from translations of preceding sentences has an impact on the translations of subsequent sentences. We further study what roles the dynamic and topic cache play in the translation process. For this aim, we calculate the average number of words in translations generated by $RNNSearch*_{+Cd,Ct}$ that are also in the caches. During the counting process, stop words and “UNK” are removed from sentence and document translations. Table 4 shows the results. If only the topic cache is used ([ $document \in [Ct]$ , $sentence \in (Ct)$ ] in Table 4), the cache still can provide useful information to help NMT translate sentences and documents. 28.3 words per document and 2.39 words per sentence are from the topic cache.


A positive coefficient reflects an increased chance that an edge between two nodes with respective properties, like group affiliation, given all other parameters unchanged, is formed. Or, put differently, a positive coefficient implies that the probability of observing a network with a higher number of corresponding pairs relative to the hypothetical baseline network, is higher than to observe the baseline network itself  . For an intuitive interpretation, log-odds value of 0 corresponds to the even chance probability of INLINEFORM0 . Log-odds of INLINEFORM1 correspond to an increase of probability by INLINEFORM2 , whereas log-odds of INLINEFORM3 correspond to a decrease of probability by INLINEFORM4 . The computational challenges of estimating ERGMs is to a large degree due to the estimation of the normalizing constant. The number of possible networks is already extremely large for very small networks and the computation is simply not feasible. Therefore, an appropriate sample has to be found, ideally covering the most probable areas of the probability distribution. For this we make use of a method from the Markov Chain Monte Carlo (MCMC) family, namely the Metropolis-Hastings algorithm.


Experimental evaluation ::: Experimental settings	We conducted our experiments on the CMU ARCTIC database , which contains parallel recordings of professional US English speakers sampled at 16 kHz. One female (slt) was chosen as the target speaker and one male (bdl) and one female (clb) were chosen as sources. We selected 100 utterances each for validation and evaluation, and the other 932 utterances were used as training data. For the TTS corpus, we chose a US female English speaker (judy bieber) from the M-AILABS speech dataset  to train a single-speaker Transformer-TTS model. With the sampling rate also at 16 kHz, the training set contained 15,200 utterances, which were roughly 32 hours long. The entire implementation was carried out on the open-source ESPnet toolkit , , including feature extraction, training and benchmarking. We extracted 80-dimensional mel spectrograms with 1024 FFT points and a 256 point frame shift. The base settings for the TTS model and training follow the Transformer.v1 configuration in , and we made minimal modifications to it for VC.


These filters rule out around 95% of the raw data and we end up with 21.4M news articles, 12,000 of which are randomly sampled for validation. We pretrain the model for 10 epochs and evaluate its performance on the validation set at the end of each epoch. The model with the highest ROUGE-L score is selected. During pretraining, we use a dropout rate of 0.3 for all inputs to transformer layers. The batch size is 1,920. We use RAdam  as the optimizer, with a learning rate of $10^{-4}$. Also, due to the different numerical scales of the positional embedding and initialized sentence piece embeddings, we divide the positional embedding by 100 before feeding it into the transformer. The beam width is set to 5 during inference. Finetuning. During finetuning, we keep the optimizer, learning rate and dropout rate unchanged as in pretraining. The batch size is 32 for all datasets. We pick the model with the highest ROUGE-L score on the validation set and report its performance on the test set. Our strategy of Pretraining with unlabeled Lead-3 summaries is called PL.


Experiments	We introduce our setups of the CommonsenseQA dataset  , present the baseline methods, and finally analyze experimental results.


Conclusion	In this paper, we introduced the problem of conversational skill discovery in speech-based assistants and presented an approach to enable users to find skills. To this end, we started with a rule-based agent and improved it by using RL, enabling the agent to adapt to different user attributes and conversational styles. We compared popularity based, rule-based and RL-based model conversational agents by deploying them in a real production setting and showed that the RL agent learns to adapt its policy to achieve a higher success rate with shorter dialogs. For future work, we plan to further personalize the dialog policy based on user attributes and conversational preferences, and investigate richer state representations. Furthermore, we plan to explore the impact of evolving attributes and preferences on the learned policies.


Experiments ::: Baselines	For comparison, we choose two state-of-the-art baselines and their variants. E2ECM : In dialogue policy maker, it adopts a classic classification for skeletal sentence template. In our implement, we construct multiple binary classifications for each act to search the sentence template according to the work proposed by . CDM : This approach designs a group of classifications (two multi-class classifications and some binary classifications) to model the dialogue policy. E2ECM+RL: It fine tunes the classification parameters of the dialogue policy by REINFORCE . CDM+RL: It fine tunes the classification of the act and corresponding parameters by REINFORCE . In order to verify the performance of the dialogue policy maker, the utterance encoder and dialogue belief tracker of our proposed model and baselines is the same, only dialogue policy maker is different.


We use three tags $\mathbf {B}_+$, $\mathbf {E}_{S,+}$ and $\mathbf {A}_+$ to encode such information above. The second sentiment span with positive ($+$) polarity contains a two-word target “Shin Lim”. The word “and” appearing before such target takes a tag $\mathbf {B}_+$. The words “perform amazing magic” appearing after such target take a tag $\mathbf {A}_+$ at each position. As for the target, the word “Shin” at the beginning of the target takes tags $\mathbf {B}_+$ and $\mathbf {E}_{B,+}$, while the word “Lim” at the end of the target takes tags $\mathbf {E}_{E,+}$ and $\mathbf {A}_+$. The third sentiment span with neutral (0) polarity contains a single-word target “AGT”. Similarly, we use three tags $\mathbf {B}_0$, $\mathbf {E}_{S,0}$ and $\mathbf {A}_0$ to represent such single word target. The word “on” appearing before such target takes a tag $\mathbf {B}_0$.


Prediction of these dialogue acts in a real-time setting can be leveraged to generate appropriate automated agent responses to similar situations. Additionally, important patterns can emerge from analysis of the fine-grained acts in a dialogue in a post-prediction setting. For example, if an agent does not follow-up with certain actions in response to a customer's question dialogue act, this could be found to be a violation of a best practice pattern. By analyzing large numbers of dialogue act sequences correlated with specific outcomes, various rules can be derived, i.e. "Continuing to request information late in a conversation often leads to customer dissatisfaction." This can then be codified into a best practice pattern rules for automated systems, such as "A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation." In this work, we are motivated to predict the dialogue acts in conversations with the intent of identifying problem spots that can be addressed in real-time, and to allow for post-conversation analysis to derive rules about conversation outcomes indicating successful/unsuccessful interactions, namely, customer satisfaction, customer frustration, and problem resolution.


Various approaches, analogies, and metaphors have been taken in this area, including “datasheets for datasets” , ”model cards” , “data statements” , “nutrition labels” , a “bill of materials” , “data labels” , and “supplier declarations of conformity” . Many go far beyond the concerns we have raised around human-labeled training data, as some are also (or primarily) concerned with documenting other forms of training data, model performance and accuracy, bias, considerations of ethics and potential impacts, and more. We discuss how our findings relate to this broader emerging area more in the concluding discussion.


Evaluation	In this section, we present the collated results of our experiments. To show that our model achieves higher accuracy than the baseline model and on a smaller training dataset, we first fix the test dataset. Our test dataset, as mentioned before, consists of 5000 tweets. We conducted our experiments on an Intel Core i5 machine (4 cores), with 8 GB RAM. The following are the accuracies of the baseline model on a training set of 200K tweets:  We filtered the training set with a subjectivity threshold of 0.5. By doing this, we saw that the number of tweets reduced to approximately 0.6 million tweets from an earlier total of 1.6 million. We then trained our model described in earlier sections on a 100K tweets randomly picked from this filtered training dataset, and observed the following accuracies:  Note that all the accuracies in the tables above have been recorded as the average of 3 iterations of our experiment. We achieve higher accuracy for all feature vectors, on all classifiers, and that too from a training dataset half the size of the baseline one. We now see the intricacies of the subjectivity threshold parameter.


Experiments ::: Experimental Setups ::: Knowledge Distillation	Previous work on non-autoregressive generation ,  has used translations produced by a pre-trained autoregressive NMT model as the training data, noting that this can significantly improve the performance. We analyze the impact of distillation in § SECREF45.


We however decided to keep them in the final corpus, as they could be useful in specific tasks and are easy to filter out otherwise. Besides the normalization described in SECREF13, no cleaning nor post-processing is applied to the sentences. This is a deliberate choice to avoid losing any information that could be pertinent for a given task or for further selection. As a result, the mean letter density is 80% and only 61% of sentences both start with an uppercase letter and end with a common punctuation mark (.!?). Finally, although we performed no human validation per se, we actively monitored the crawling process to spot problematic domains early. This allowed to blacklist some domains entirely, for example those serving embedded PDFs (impossible to parse properly) or written in very close German dialects.


For the final classification, an additional linear layer is added to output predicted class distributions. For noise handling, we experiment with the Confusion Matrix model by  and the Cleaning model by . We repeat all the Bi-LSTM experiments 20 times and report the average F1-score (following the approach by ) and the standard error. The BERT model is obtained by fine-tuning the pre-trained BERT embeddings on NER data with an additional untrained CRF classifier. We fine-tuned all the parameters of BERT including that of the CRF end-to-end. This has been shown to give better performance than using word features extracted from BERT to train a classifier . The evaluation result is obtained as an average of 5 runs, we report the F1-score and the standard error in the result section.


Finally, the difference between the best results and the results using the Local features is lower than for English, even though the Local results are similar to those obtained with the English datasets (except for Turkish, but this is due to the significantly smaller size of the data, as shown in Table TABREF7 ). We believe that all these four issues are caused, at least partially, by the lack of domain-specific clustering features used for the multilingual experiments. In other words, while for the English experiments we leveraged the Yelp dataset to train the clustering algorithms, in the multilingual setting we first tried with already available clusters induced from the Wikipedia. Thus, it is to be expected that the gains obtained by clustering features obtained from domain-specific data such as Yelp would be superior to those achieved by the clusters trained on out-of-domain data. In spite of this, Table TABREF20 shows that our system outperforms the best previous approaches across the five languages. In some cases, such as Turkish and Russian, the best previous scores were the baselines provided by the ABSA organizers, but for Dutch, French and Spanish our system is significantly better than current state-of-the-art.


Another advantage of social networks is that they are widely used. Twitter claims that they had 313 million monthly active users in the second quarter of 2016 . This characteristic will enlarge the scope of detection and is extremely helpful when dealing with cross domain attacks because tweets from multiple places can be leveraged. The large number of users of social networks will also guarantee the sensitivity of the model. However, because of the large number of users, a huge quantity of tweets will be generated in a short time, making it difficult to manually annotate the tweets, which makes unsupervised or weakly-supervised models much more desirable. In the Twitter data that we collected there are three kinds of tweets. Firstly are tweets that are actually about a cyberattack. For example, someone tweeted "Can't sign into my account for bank of America after hackers infiltrated some accounts." on September 19, 2012 when a attack on the website happened. Secondly are tweets about some random complaints about an entity like "Death to Bank of America!!!! RIP my Hello Kitty card... " which also appeared on that day. Lastly are tweets about other things related to the bank.


The second point follows given that words embed on a manifold, sentences traces out paths on the manifold, and the difference equation the RNN solves bears a striking resemble to the first order equation for parallel transport, DISPLAYFORM0   where INLINEFORM0 is the INLINEFORM1 -th hidden state encountered when reading over a sentence and INLINEFORM2 is the RNN conditioned by the INLINEFORM3 -th word, INLINEFORM4 , acting on the hidden state. Since sentences trace out a path on the word manifold, and parallel transport operators for representations of the word manifold take values in the group, the RNN must parallel transport hidden states either on the group itself or on a base space, INLINEFORM5 , equipped with some word field, INLINEFORM6 , that connects the path in the base space to the path on the word manifold. Leveraging these results, we propose two new technologies. First, we propose a class of recurrent-like neural networks for NLP tasks that satisfy the differential equation DISPLAYFORM0  where DISPLAYFORM0   and where INLINEFORM0 and INLINEFORM1 are learned functions. INLINEFORM2 corresponds to traditional RNNs, with INLINEFORM3 .


Experiments ::: Analysis on Decoding Speed ::: How does sentence length affect the decoding speed?	Next, we examine if sentence length is a major factor affecting the decoding speed. We bucket the test data by the target sentence length. From Fig. FIGREF44, we can see that as the sentence length increases, FlowSeq achieves almost constant decoding time while Transformer has a linearly increasing decoding time. The relative decoding speed up of FlowSeq versus Transformer linearly increases as the sequence length increases. The potential of decoding long sequences with constant time is an attractive property of FlowSeq.


Proposed Model ::: Overall Architecture	The process of KANE is illustrated in Figure FIGREF2. We introduce the architecture of KANE from left to right. As shown in Figure FIGREF2, the whole triples of knowledge graph as input. The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. This method can recursively propagate the embeddings of entities from an entity's neighbors, and aggregate the neighbors with different weights. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification.


Method ::: Hierarchically-Refined Review Encoder ::: Attention Inference Layer	In the attention inference layer, we model the dependencies between the original review and the summary with multi-head dot-product attention.Each head produces an attention matrix $\mathbf {\alpha } \in \mathbb {R}^{d_h \times 1}$ consisting of a set of similarity scores between the hidden state of each token of the review text and the summary representation. The hidden state outputs are calculated by where $\mathbf {W}_i^Q \in \mathbb {R}^{d_{h} \times \frac{d_{h}}{k}}$, $\mathbf {W}_i^K \in \mathbb {R}^{d_{h} \times \frac{d_{h}}{k}}$ and $\mathbf {W}_i^V \in \mathbb {R}^{d_{h} \times \frac{d_{h}}{k}}$ are model parameters. $Q$, $K$ and $V$ represent Query, Key and Value, respectively. $k$ is the number of parallel heads and $i \in [1,k]$ indicates which head is being processed.


Standard Yorùbá has 25 letters without the Latin characters c, q, v, x and z. There are 18 consonants (b, d, f, g, gb, j[dz], k, l, m, n, p[kp], r, s, ṣ, t, w y[j]), 7 oral vowels (a, e, ẹ, i, o, ọ, u), five nasal vowels, (an, $ \underaccent{\dot{}}{e}$n, in, $ \underaccent{\dot{}}{o}$n, un) and syllabic nasals (m̀, ḿ, ǹ, ń). Yorùbá is a tone language which makes heavy use of lexical tones which are indicated by the use of diacritics. There are three tones in Yorùbá namely low, mid and high which are represented as grave ($\setminus $), macron ($-$) and acute ($/$) symbols respectively. These tones are applied on vowels and syllabic nasals.


Dictionary-based PIE Extraction ::: String-based Extraction Methods ::: Additional Steps	For all string match-based methods, ways to improve performance are implemented, to make them as competitive as possible. Rather than doing exact string matching, we also allow words to be separated by something other than spaces, e.g. nuts-and-bolts for nuts and bolts. Additionally, there is an option to take into account case distinctions. With the case-sensitive option, case is preserved in the idiom lists, e.g. coals to Newcastle, and the string matching is done in a case-sensitive manner. This increases precision, e.g. by avoiding PIEs as part of proper names, but also comes at a cost of recall, e.g. for sentence-initial PIEs. Thirdly, there is the option to allow for a certain number of intervening words between each pair of words in the PIE. This should improve recall, at the cost of precision. For example, this would yield the true positive make a huge mountain out of a molehill for make a mountain out of a molehill, but also false positives like have a smoke and go for have a go.


Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation	Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.


Los diccionarios consisten en entradas de pares: POS$_\Phi $ y una lista de palabras y signos asociados, formalmente POS$_\Phi $ $\rightarrow $ $l$(POS$_\Phi )=(l_1,l_2,...,l_j)$. Se reemplaza aleatoriamente cada POS$_\Phi $ por una palabra de $l$ que corresponda a la misma clase gramatical. Las etiquetas POS$_\lambda $ serán reemplazadas por las palabras producidas por Word2vec $L(Q)$. Si ninguna de las palabras de $L(Q)$ tiene la forma sintáctica exigida por POS$_\lambda $, empleamos la biblioteca PATTERN para realizar conjugaciones o conversiones de género y/o número y reemplazar correctamente POS$_\lambda $.


We then process every sentence by checking if it contains “die” or “dat”, and if so, add a training example for every occurrence of this word in the sentence, where a single occurrence is masked. For the test set for example, this resulted in about 289k masked sentences. We then test two different approaches for solving this task on this dataset. The first approach is making the BERT models use their MLM task and guess which word should be filled in this spot, and check if it has more confidence in either “die” or “dat” (by checking the first 2,048 guesses at most, as this seemed sufficiently large). This allows us to compare the zero-shot BERT models, i.e. without any fine-tuning after pre-training, for which the results can be seen in Table TABREF7. The second approach uses the same data, but creates two sentences by filling in the mask with both “die” and “dat”, appending both with the [SEP] token and making the model predict which of the two sentences is correct. The fine-tuning was performed using 4 Nvidia GTX 1080 Ti GPUs and evaluated against the same test set of 399k utterances.


For the SVMs in the generalization-dataset experiments, we tuned the hyperparameters on novel-9 with a grid search, and found the optimal setting as: stopping condition tol is 1e-5, at a max-iteration of 1,500.


Related Work	Work toward learning reusable sentence encoders can be traced back at least as far as the multitask model of  , but has seen a recent surge in progress with the successes of CoVe  , ULMFit  , ELMo  , and the Transformer LM  . However, each uses a different model and dataset from the others, so while these works serve as existence proofs that effective reusable sentence encoders are possible, they do not address the question of what task or tasks should be used to create them. The revival of interest in sentence encoder pretraining is recent enough that relatively little has been done to understand the relative merits of these models, though two exceptions stand out. In unpublished work,  offer an analysis of the relative strengths of translation and language modeling using a single architecture and training dataset. They find that encoders trained as language models reliably uncover the most syntactic structure, even when they are trained on a strict subset of the data used for a comparable translation model.  offer a deeper investigation of model design issues for ELMo, showing that all of the standard architectures for sentence encoding can be effectively pretrained with broadly similar performance, and that all learn reasonably good representations of the morphological and syntactic properties of sentences.


Experiments ::: Variance reduction	As stated in Sec. SECREF3, the motivation of using the average reward baseline is for better variance reduction. Here we show it indeed is better in practice. The gradient variance is calculated as follows. At the end of each epoch, we take the saved model and run through the training set. We get the gradients from each training batch and calculate the variance for each parameter gradient across batches. To get a single value, we take the average of all the parameters. A mathematic expression of this process is: where $i$ is the index of each parameter; $b$ is the index of each batch; $\theta $ is the network parameters; $\text{grad}_{\theta _i}^b$ is the gradient of $\theta _i$ at batch $b$. As shown in Fig. FIGREF16, our method is always getting lower variance than SCST.


E\left[ Y_{j} | \mathbf {w}_{j}, \alpha , \phi ,\eta , \sigma ^{2} \right] \approx E_{q} \left[\mu \left(\eta ^{T} \bar{\mathbf {z}}_{j} \right)\right],


Datasets and Evaluation Metrics	We use the DDI corpus of the 2013 DDIExtraction challenge  to train and test our model. The DDIs in this corpus are classified as five types. We give the definitions of these types and their example sentences, as shown in table TABREF4 . This standard dataset is made up of training set and testing set. We use the same metrics as in other drug-drug interaction extraction literature  ,  ,  ,  ,  ,  : the overall precision, recall, and F1 score on testing set. INLINEFORM0 denotes the set of {False, Mechanism, Effect, Advise, Int}. The precision and recall of each INLINEFORM1 are calculated by DISPLAYFORM0 DISPLAYFORM1  Then the overall precision, recall, and F1 score are calculated by DISPLAYFORM0  Besides, we evaluate the captured feature vectors with t-SNE  , a visualizing and intuitive way to map a high dimensional vector into a 2 or 3-dimensional space. If the points in a low dimensional space are easy to be split, the feature vectors are believed to be more distinguishable.


New Terminology: Potentially Idiomatic Expression (PIE)	The ambiguity of phrases like wake up and smell the coffee poses a terminological problem. Usually, these phrases are called idiomatic expressions, which is suitable when they are used in an idiomatic sense, but not so much when they are used in a literal sense. Therefore, we propose a new term: potentially idiomatic expressions, or PIEs for short. The term potentially idiomatic expression refers to those expressions which can have an idiomatic meaning, regardless of whether they actually have that meaning in a given context. So, see the light is a PIE in both `After another explanation, I finally saw the light' and `I saw the light of the sun through the trees', while it is an idiomatic expression in the first context, and a literal phrase in the latter context. The processing of PIEs involves three main challenges: the discovery of (new) PIE types, the extraction of instances of known PIE types in text, and the disambiguation of PIE instances in context. Here, we propose calling the discovery task simply PIE discovery, the extraction task simply PIE extraction, and the disambiguation task PIE disambiguation.


Question Aware Context Encoder	The question aware context embeddings $\mathbf {C}$ is generated by the context encoder, while HAS-QA do not limit the use of context encoder. We choose a simple but efficient context encoder in this paper. It takes advantage of previous works  ,  , which contains the character-level embedding enhancement, the bi-directional attention mechanism  and the self-attention mechanism  . We briefly describe the process below . Word Embeddings: use size 300 pre-trained GloVe  word embeddings. Char Embeddings: encode characters in size 20, which are learnable. Then obtain the embedding of each word by convolutional layer and max pooling layer. Context Embeddings: concatenate word embeddings and char embeddings, and apply bi-directional GRU  to obtain the context embeddings. Both question and paragraph get their own context embeddings. Question Aware Context Embeddings: use bi-directional attention mechanism from the BiDAF  to build question aware context embeddings. Additionally, we subsequently apply a layer of self-attention to get the final question aware context embeddings.


Evaluation ::: Automatic Metrics	For automatically evaluating our methods, we propose to use widely used metric for image/video captioning. This is because the proposed CommonGen task can be regarded as also a caption task where the context are incomplete scenes with given concept-sets. Therefore, we choose BLEU-3/4 , ROUGE-2/L , CIDEr , and SPICE  as the main metrics. Apart from these classic metrics, we also include a novel embedding-based metric named BERTScore . To make the comparisons more clear, we show the delta of BERTScore results by subtracting the score of merely using input concept-sets as target sentences, named $\triangle $BERTS. To have an estimation about human performance in each metric, we iteratively treat every reference sentence in dev/test data as the prediction to be compared with all references (including itself). That is, if a model has the same reasoning ability with average performance of our crowd workers, its results should exceed this “human bound”.


Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM	Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.


Specifically, dialog act $$ is defined as the combination of intent $$ and slot-value pairs $\lbrace (s_i, v_i)\rbrace ^P_{i=1}$: where $P$ is the number of pairs, which varies in different dialog acts. Intents are usually used to distinguish different types of system actions. Typical examples include inform, request, confirm, select Slot-value pairs indicate the category and content of the information to express in the utterance, respectively. The goal of NLG is to translate $$ into a natural language response $= [x_1, \cdots , x_T]$, where $T$ is the sequence length. In Figure FIGREF2 (b), we show an example of the dialog act: $\textit {\texttt {confirm}~(name=Hilton, area=center)}$, and the corresponding natural language response is “Let me confirm that you are searching for Hilton in the center area”.


Conversational Skill Discovery ::: Problem Formulation	Conversational skill discovery, similar to other goal-oriented dialog systems, can be formalized as a Markov Decision Process (MDP) . An MDP is a tuple $<\mathcal {S}, \mathcal {A}, \mathcal {P}, \mathcal {R}, \mathcal {\gamma }>$, where $\mathcal {S}$ is the state space, $\mathcal {A}$ is the action space, $\mathcal {P}$ is the transition probability function, $\mathcal {R}$ is the reward function, and $\mathcal {\gamma }$ is the discount factor. In this framework, at each time step t, the agent observes state $s_t \in \mathcal {S}$ and selects action $a_t \in \mathcal {A}$ according to its policy ($\pi : \mathcal {S} \rightarrow \mathcal {A}$). After performing the selected action, the agent receives the next state $s_{t+1}$ and a scalar reward $r_t$. The trajectory restarts after the agent reaches a terminal state.


\nonumber \end{split}$$   (Eq. 7)  where past QA pairs $q_1, a_1, \dots , q_{k-1}, a_{k-1}$ in $C$ are concatenated and separated by two special tokens [Q] and [A] and then concatenate with the current question $q_k$ as the left side of BERT and the right side is the review document.


We took a subset of the instances equally distributed with respect to their class from the training set (330 instances for each class) and test set (71 instances for each class). As shown in Table TABREF31 , our classifier was able to correctly predict the underrepresented classes much better, although the overall accuracy is lower (59.9%). The result of this analysis clearly indicates that class imbalance has a negative impact on the system performance.


Introduction	Conversational agents or dialogue systems development are gaining more attention from both industry and academia  ,  in the latest years. Some works tried to model them into domain-specific tasks such as customer service  ,  , and shopping assistance  . Other works design a multi-purpose agents such as SIRI, Amazon Alexa, and Google Assistance. This domain is a well-researched area in Human-Computer Interaction research community but still become a hot topic now. The main development focus right now is to have an intelligent and humanizing machine to have a better engagement when communicating with human  . Having a better engagement will lead to higher user satisfaction, which becomes the main objective from the industry perspective. In this study, we will only focus on textual conversational agent or chatbot, a conversational artificial intelligence which can conduct a textual communication with a human by exploiting several natural language processing techniques. There are several approaches used to build a chatbot, start by using a simple rule-based approach  ,  until more sophisticated one by using neural-based technique  ,  .


Sentiment analysis is contextual mining of text which identifies and extracts subjective information in the source material and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. To resolve the risks and fraud problems, it is important not only to analyze the social-network opinion, but also to scan the smart contract vulnerability detection.


Model components ::: Attention Mechanism	Attention mechanism was first developed by , in their paper “Neural Machine Translation by Jointly Learning to Align and Translate” which takes in as a natural extension of their previous work on the sequence to sequence Encoder-Decoder model. Attention is proposed as a solution to mitigate the limitation of the Encoder-Decoder architecture which encodes the input sequence to one fixed length vector from which the output is decoded at each time step. This problem seems to be more of a issue when decoding long sequences. Attention is proposed as a singular method to both align and translate. Alignment is the problem in machine translation that seeks to find which parts of the input sequence are relevant to each word in the output, whereas translation is the process of using the relevant information to select the appropriate output.


Conclusions and applications	We proposed a new method for clustering of visits in health centers based on descriptions written by doctors. We validated this new method on a new large corpus of Polish medical records. For this corpus we identified medical concepts and created their embeddings with GloVe algorithm. The quality of the embeddings was measured by the specific analogy task designed specifically for this corpus. It turns out that analogies work well, what ensures that concept embeddings store some useful information. Clustering was performed on visits embedding created based on word embedding. Visual and numerical examination of derived clusters showed an interesting structure among visits. As we have shown obtained segments are linked with medical diagnosis even if the information about recommendations nor diagnosis were not used for the clustering. This additionally convinces that the identified structure is related to some subgroups of medical conditions. Obtained clustering can be used to assign new visits to already derived clusters. Based on descriptions of an interview or a description of patient examination we can identify similar visits and show corresponding recommendations.


The Overfitting-Resistant Classifier	The classifier that we use to make a decision based on these features is scikit-learn's LinearSVC, using the default parameters in scikit-learn 0.19.1. (In Section SECREF4 , we discuss other models and parameters that we tried.) This classifier makes effective use of the features while being simple enough to avoid some amount of overfitting. One aspect of the classifier that made a noticeable difference was the scaling of the features. We tried INLINEFORM0 and INLINEFORM1 -normalizing the columns of the input matrix, representing the values of each feature, and decided on INLINEFORM2 normalization. We took advantage of the design of our features and the asymmetry of the task as a way to further mitigate overfitting. All of the features were designed to identify a property that INLINEFORM0 has and INLINEFORM1 does not, as is the case for the discriminative examples, so they should all make a non-negative contribution to a feature being discriminative. We can inspect the coefficients of the features in the SVC's decision boundary.


Datasets ::: Persona-chat and Cornell-movie corpus	We use two datasets to train our chatbot model. The first one is the Persona-chat  which contains dialogues between two people with different profiles that are trying to know each other. It is complemented by the Cornell-movie dialogues dataset , which contains a collection of fictional conversations extracted from raw movie scripts. Persona-chat's sentences have a maximum of 15 words, making it easier to learn for machines and a total of 162,064 utterances over 10,907 dialogues. While Cornell-movie dataset contains 304,713 utterances over 220,579 conversational exchanges between 10,292 pairs of movie characters.


Acknowledgments	This work has received funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from the TELEPARES-UDC (FFI2014-51978-C2-2-R) and ANSWER-ASAP (TIN2017-85160-C2-1-R) projects from MINECO, and from Xunta de Galicia (ED431B 2017/01).


If removing this word already changes the model's prediction – that is, $M(\mathbf {x}_{w_i = \texttt {[MASK]}}) \ne y$ –, we select a random rare synonym $\hat{w}_i \in S(w_i)$ and add $(\mathbf {x}_{w_i = \hat{w}_i}, y)$ to the test set. Otherwise, we repeat the above procedure; if the label still has not changed after masking up to 5 words, we discard the corresponding entry. All so-obtained test set entries $(\mathbf {x}_{w_{i_1} = \hat{w}_{i_1}, \ldots , w_{i_k} = \hat{w}_{i_k} }, y)$ have the following properties: If each $w_{i_j}$ is replaced by a [MASK] token, the entry is classified incorrectly by $M$. In other words, understanding the words $w_{i_j}$ is essential for $M$ to determine the correct label.


A Large-Scale Corpus for Conversation Disentanglement	Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 80% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.


Our approach allows to create a small number of seed patterns in advance, then explore other patterns through process of grammar learning. A similar bootstrapping semi-automatic approach to ontology learning was developed in  , where the user validates lexicalizations of a particular relation to learn new instances, and in  , where the user validates newly identified terms, while in our approach the user validates grammar rules to learn the composition of whole sentences. A similar approach with combining DBpedia with Wikipedia for superised learning has been taken in  , however their focus is more on lexicalization of relations and classes.


Tree Kernels compute the number of common substructures between two trees $T_1$ and $T_2$ without explicitly considering the whole fragment space . Suppose the set $\mathcal {F}=\lbrace f_1,f_2, \dots , f_{|\mathcal {F}|} \rbrace $ be the tree fragment space and $\mathcal {X}_i(n)$ be an indicator function that is 1 if the $f_i$ rooted at node $n$ and equals to 0, otherwise. Now, tree kernel over $T_1$ and $T_2$ is defined as below  : where $N_{T_1}$ and $N_{T_2}$ are the set of nodes of $T_1$ and $T_2$, respectively and which shows the number of common fragments rooted in $n_1$ and $n_2$ nodes. Different tree kernels vary in their definition of $\Delta $ function and fragment type. There are three important characterizations of fragment type  : SubTree, SubSet Tree and Partial Tree. A SubTree is defined by taking a node of a tree along with all its descendants.


Semantic parsing takes advantage of RNN architectures to parse MWPs directly into equations or expressions in a math-specific language , . RNNs have shown promising results, but they have had difficulties balancing parenthesis, and also, sometimes incorrectly choose numbers when generating equations. Rehman et al.  used POS tagging and classification of equation templates to produce systems of equations from third-grade level MWPs. Most recently, Sun et al.  used a Bi-Directional LSTM architecture for math word problems. Huang et al.  used a deep reinforcement learning model to achieve character placement in both seen and novel equation templates. Wang et al.  also used deep reinforcement learning.


We then use the gold standard from the annotation project to assess the performance of multiple neural classification models trained exclusively on Term Frequency – Inverse Document Frequency (TF-IDF) vectorized EHR data, described in section 4. To further improve the performance of our model, we incorporate domain-relevant MWEs identified using all in-house data.


We got the best quality with regard to the F1-score with 256 as the size of the hidden states of the LSTMs, 1024 as word embedding size, and a dropout of 30 %. For the batch size, we used 64. We optimized our single-task model trained on real data in the same manner as the single-task baseline, except that we used 64 epochs. In the multi-task learning approach, we trained both tasks for 10 epochs. We use for $m$ (the instance multiplicator of the synthetic dataset) such a value that the synthetic dataset has nearly the size of one-tenth of the out-of-domain dataset. Because of long training times, we were not able to optimize the hyperparameters. We chose 256 as the size of the hidden states of the LSTMs, 1024 as word embedding size, and 50 % for the dropout and were not able to run multiple runs. For $n$ (the number of instances that are trained simultaneously on the GPU), we chose 128 and for $t$ (number of groups after that the model weights are updated) we chose 11.


The approximation error becomes larger when the derivative is evaluated further from the critical point. In contrast, the automatic differentiation (in both modes) yields the exact result of 0.


The AQA-QR rewrites are those whose corresponding answers are evaluated as AQA TopHyp in Table 1 . These single rewrites alone outperform the original SearchQA queries by 2% on the test set. We analyze the top hypothesis instead of the final output of the full AQA agent to avoid confounding effects from the answer selection step. These rewrites look different from both the Base-NMT and the SearchQA ones. For the example above AQA-QR's top hypothesis is What is name gandhi gandhi influence wrote peace peace?. Surprisingly, 99.8% start with the prefix What is name. The second most frequent is What country is (81 times), followed by What is is (70) and What state (14). This is puzzling as it occurs in only 9 Base-NMT rewrites, and never in the original SearchQA questions. We speculate it might be related to the fact that virtually all answers involve names, of named entities (Micronesia) or generic concepts (pizza). AQA-QR's rewrites seem less fluent than both the SearchQA and the Base-MT counterparts.


Satisfaction aspect has three categories, including affect, ethics and behaviour, and accessibility. Affect is the most suitable assessment categories for EAC. This category asses several quality aspects such as, chatbots' ability to convey personality, give conversational cues, provide emotional information through tone, inflexion, and expressivity, entertain and/or enable the participant to enjoy the interaction and also read and respond to moods of human participant  . Ethic and behaviour category focuses on how a chatbot can protect and respect privacy  . Other quality aspects, including sensitivity to safety and social concerns and trustworthiness  . The last categories are accessibility, which the main quality aspect focus to assess the chatbot ability to detect meaning or intent and, also responds to social cues .


Introduction ::: blackTraditional Learning Based Text-to-image Synthesis	In the early stages of research, text-to-image synthesis was mainly carried out through a search and supervised learning combined process , as shown in Figure FIGREF4. In order to connect text descriptions to images, one could use correlation between keywords (or keyphrase) & images that identifies informative and “picturable” text units; then, these units would search for the most likely image parts conditioned on the text, eventually optimizing the picture layout conditioned on both the text and the image parts. Such methods often integrated multiple artificial intelligence key components, including natural language processing, computer vision, computer graphics, and machine learning. The major limitation of the traditional learning based text-to-image synthesis approaches is that they lack the ability to generate new image content; they can only change the characteristics of the given/training images. Alternatively, research in generative models has advanced significantly and delivers solutions to learn from training images and produce new visual content. For example, Attribute2Image  models each image as a composite of foreground and background. In addition, a layered generative model with disentangled latent variables is learned, using a variational auto-encoder, to generate visual content.


To investigate whether a BiLSTM can assist the model, we apply another BiLSTM to the three fusion representations in Formula 23 respectively and then put them together. The results are shown in the second column in Table 3 , which indicate that the BiLSTM does not help improve the performance of the models.


Conclusions	Zipf's law is one of the most universal statistics of natural languages. However, it may be too universal. While it may not strictly apply to sequences of independent random symbols with random spacings , it appears to describe random texts that conserve most of the correlations between successive symbols, as accurately as it describes texts written in real languages. Further, Heaps-Herdan law and the degree distribution of the adjacency network, appear to be consequences of Zipf's law, and are, thus, as universal. In this work we studied 91 texts in seven different languages, as well as random texts constructed by randomizing the spacings between words without altering the order of the letters in the text. We find that they are all well described by the universal laws. However, we also found that the distribution of clustering coefficients of the networks of each text appears to vary from one language to another, and to distinguish random texts from real languages. The nodes that vary the most among the distributions of $C(k)$ are those for which $C(k)$ is equal to 0 or 1.


Task Definition	We consider a shared channel in which a group of people are communicating by sending messages that are visible to everyone. We label this data with a graph in which messages are nodes and edges indicate that one message is a response to another. Each connected component is a conversation. Figure shows an example of two entangled conversations and their graph structure. It includes a message that receives multiple responses, when multiple people independently help BurgerMann, and the inverse, when the last message responds to multiple messages. We also see two of the users, delire and Seveas, simultaneously participating in two conversations. This multi-conversation participation is common. The example also shows two aspects of IRC we will refer to later. Directed messages, an informal practice in which a participant is named in the message. These cues are useful for understanding the discussion, but only around 48% of messages have them. System messages, which indicate actions like users entering the channel. These all start with ===, but not all messages starting with === are system messages, as shown by the second message in Figure .


Ablation Study	We conducted another two groups of experiments to investigate the contribution of the three different types of information: narrative sequence, sentiment evolution and commonsense knowledge. First, we measure the accuracy of only using one type of information at a time and describe the result in Table 3 . When we use just one type of information, the performances are worse than when using all of the information, suggesting a single type of information is insufficient for story ending selection. We also measure the performance of our model by stripping one type of information at a time and display the results in Table 4 . We observe that by removing the narrative sequence information, the model performance decreases most significantly. We suspect this is because the narrative chain is the key element that differentiates a story from other types of writing. Therefore, removing narrative sequence information makes it difficult to predict the story ending. If we only use the narrative sequence information, the performance is 85.3%. When commonsense knowledge is added to the model on top of the narrative sequence information, the performance improves to 87.2% which is statistically significant. When sentiment evolution information is added, the model only improves to 87.6%.


Furthermore, $\text{SRL}_{\text{SAOKE}}$ and $\text{HypeNet}$ methods suffer from the OOV problem, such as unfamiliar words/phrases like the person name and school name in the last case of Table 6 . In this situation they may fail to produce a reasonable result. Logician is able to cope with unfamiliar words/phrases by exploiting the context information using deep RNN network with the help of copy mechanism.


to conduct an adversarial attack on character-level NMT by employing differentiable string-edit operations. The method of generating adversarial examples was same in their previous work  . Compared with Belinkov et al.  , the authors demonstrated that black-box adversarial examples were much weaker than black-box ones in most cases. Iyyer et al.  crafted adversarial examples by the use of SCPNS they proposed. They designed this model for generating syntactically adversarial examples without decreasing the quality of the input semantics. The general process mainly relied on the encoder-decoder architecture of SCPNS. Given a sequence and a corresponding target syntax structure, the authors encoded them by a bidirectional LSTM model and decoded by LSTM model augmented with soft attention over encoded states  and the copy mechanism  . They then modified the inputs to the decoder, aiming at incorporating the target syntax structure to generate adversarial examples. The syntactically adversarial sentences not only could fool pre-trained models, but also improved the robustness of them to syntactic variation. The authors also used crowdsourced experiment to demonstrate the validity of the generated.


Experiments ::: Predicting Individual Stock Movements	Other than predicting the S&P 500 index, we also investigate the effectiveness of our approach on the problem of individual stock prediction using the test set. We count the amounts of individual company related news events for each company by name matching, and select five well known companies with sufficient news, Apple, Citigroup, Boeing Company, Google and Wells Fargo from four different sectors, which is classified by the Global Industry Classification Standard. For each company, we prepare not only news events about itself, but also news events about the whole companies in the sector. We use company news, sector news and all financial news to predict individual stock price movements, respectively. The experimental results and news statistics are listed in Table TABREF30. The result of individual stock prediction by only using company news dramatically outperforms that of sector news and all news, which presents a negative correlation between total used amounts of news events and model performance. The main reason maybe that company-related news events can more directly affect the volatility of company shares, while sector news and all news contain many irrelevant news events, which would obstruct our ANRES model's learning the underlaying stock price movement trends.


The largest difference between the coalitions in the European Parliament and on Twitter is on the far-right, where we observe ENL and NI as isolated blocks. The results shown in Fig FIGREF44 quantify the extent to which communication in one social context (Twitter) can explain cooperation in another social context (co-voting in the European Parliament). A positive value indicates that the matching behavior in the retweet network is similar to the one in the co-voting network, specific for an individual policy area. On the other hand, a negative value implies a negative “correlation” between the retweeting and co-voting of MEPs in the two different contexts. The bars in Fig FIGREF44 correspond to the coefficients from the edge covariate terms of the ERGM, describing the relationship between the retweeting and co-voting behavior of MEPs. The coefficients are aggregated for individual policy areas by means of a meta-analysis. Overall, we observe a positive correlation between retweeting and co-voting, which is significantly different from zero. The strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets.


Materials and Methods	We describe and motivate the materials first and then proceed to the methods and motivations thereof. There are multiple ontologies available over internet with competency questions provided, but since the focus of our research is on SPARQL-OWL queries, we selected only those ontologies with CQs stated against ontology schema (T-Box). As a result we selected 5 ontologies with 234 competency questions in total. Table TABREF8 summarizes our dataset size and source of each ontology. The Software Ontology (SWO)  is included because its set of CQs is of substantial size and it was part of Ren et al.'s set of analysed CQs. The CQ sets of Dem@Care  and OntoDT  were included because they were available. CQs for the Stuff  and African Wildlife (AWO)  ontologies were added to the set, because the ontologies were developed by one of the authors (therewith facilitating in-depth domain analysis, if needed), they cover other topics, and are of a different `type' (a tutorial ontology (AWO) and a core ontology (Stuff)), thus contributing to maximising diversity in source selection.


31)   where the output embedding $W^2 \in \mathbb {R}^{d_\mathrm {word} \times V_\mathrm {ext}}$ is tied with the corresponding part of the input embedding  , and $W^1 \in \mathbb {R}^{d_\mathrm {word} \times d}$ and $b^1 \in \mathbb {R}^{d_\mathrm {word}}$ are learnable parameters. $P^v(y_t)$ is zero if $y_t$ is an out-of-vocabulary word for $V$ . The copy mechanism used in the original pointer-generator is based on the attention weights of a single-layer attentional RNN decoder  . The attention weights in our decoder stack are the intermediate outputs in multi-head attentions and are not suitable for the copy mechanism. Therefore, our model also uses additive attentions for the question and multiple passages on top of the decoder stack.


Results	The following sections give an overview of the results for all political bias prediction tasks. Some interpretations of the results are highlighted and a web application of the models is presented at the end of the section.


Conclusion and Outlook	Applying the example of metal music, our work examined the textual topics found in song lyrics and investigated the association between these topics and high-level music features. By using LDA and MDS in order to explore prevalent topics and the topic space, typical text topics identified in qualitative analyses could be confirmed and objectified based on a large text corpus. These include e.g. satanism, dystopia or disgusting objects. It was shown that musical hardness is particularly associated with harsh topics like ‘brutal death’ and ‘dystopia’, while it is negatively linked to relatively mundane topics concerning personal life and love. We expect that even stronger correlations could be found for metal-specific topics when including more genres covering a wider range of hardness/darkness values. Therefore, we suggest transferring the method to a sample including multiple genres. Moreover, an integration with metadata such as genre information would allow for the testing of associations between topics, genres and high-level audio features. This could help to better understand the role of different domains in an overall perception of genre-defining attributes such as hardness.


Some of them require noise rate to be known a priori in order to tune their models during training , . Our approach combines direction (1) and direction (3), and trains two networks jointly without knowing the noise rate. We have used five latest existing methods in our experiments for SSC. The experimental results show that they are inferior to our proposed method. In addition, , , , , , and  studied weakly-supervised DNNs or semi-supervised DNNs. But they still need some clean-labeled training data. We use no clean-labeled data.


Introduction	With the rapid development in deep learning, there is a recent boom of task-oriented dialogue systems in terms of both algorithms and datasets. The goal of task-oriented dialogue is to fulfill a user's requests such as booking hotels via communication in natural language. Due to the complexity and ambiguity of human language, previous systems have included semantic decoding  to project natural language input into pre-defined dialogue states. These states are typically represented by slots and values: slots indicate the category of information and values specify the content of information. For instance, the user utterance “can you help me find the address of any hotel in the south side of the city” can be decoded as $inform(area, south)$ and $request(address)$, meaning that the user has specified the value south for slot area and requested another slot address. Numerous methods have been put forward to decode a user's utterance into slot values. Some use hand-crafted features and domain-specific delexicalization methods to achieve strong performance , .  employs CNN and pretrained embeddings to further improve the state tracking accuracy.  extends this work by using two additional statistical update mechanisms.  uses human teaching and feedback to boost the state tracking performance.


in the form of multiple response generators), and we define a fixed time window for each major step in our pipeline, after which we interrupt the current component and use the information already computed from the sub-components in the next step, reducing total processing time. We will develop our system in three phases (Figure FIGREF1): Components marked core, core+, and core++ are to be completed by the end of phase 5, 7, and 9, respectively. These are the minimally planned components for each category, but if time permits we will advance faster. This provides us an incremental and iterative approach to build our architecture starting with the most important components, always testing included components before advancing to new ones. Our main novelties include: [topsep=2pt,itemsep=-1ex,partopsep=1ex,parsep=1ex] Using self-play strategies to train a neural response ranker. Computing a large number of metrics for both input and response, and specifically optimizing some models for our metrics. Training a separate dialog model for each user. Using a response classification predictor and a response classifier to predict and control aspects of responses. Predicting which model emits the best response before response generation.


While the original technique combines the word and sentence level attentions, our model combines the passage-level relevance $\beta ^{p_k}$ and word-level attentions $\alpha ^p_t$ by using simple scalar multiplication and re-normalization. The updated word attention is:  $$\alpha ^p_{tl} & := \frac{\alpha ^p_{tl} \beta ^{p_{k(l)} }}{\sum _{l^{\prime }} \alpha ^p_{tl^{\prime }} \beta ^{p_{k(l^{\prime })}}}.$$   (Eq. 39)


Results of Sentiment Analysis of Twitter Messages	In this section, we provide a detailed discussion of the results obtained from the sentiment analysis of the two datasets. Fig. FIGREF11 corresponds to the histogram of sentiment polarities of tweets on Coronavirus by general public. It can be seen that majority of the tweets have a neutral sentiment followed by positive. The same can be inferred from Table TABREF10 that shows that around 54$\%$ tweets are neutral, 29$\%$ positive and a mere 15$\%$ is negative. Fig. FIGREF12 corresponds to the histogram of sentiment polarities of tweets on Coronavirus by WHO. It can be seen that majority of the tweets have a neutral and positive sentiment. Table TABREF10 that shows that around 60$\%$ tweets are positive, 24$\%$ neutral and a mere 15$\%$ is negative. This shows how WHO is trying to retain the positive spirit through its social media accounts. Fig. FIGREF13 and FIGREF14 represent the histograms produced by removing the neutral tweets. It readily reiterates that the positive emotions in the tweets are higher than negative ones.


\end{split}$$   (Eq. 12)  where, $\mathbf {S}$ is the permutation set of the reference representation and the inference representation. ${o}_{utn}^{(s^{\prime })}$ and ${o}_{utn}^{(r)}$ is the frame level acoustic representation of permutation $s^{\prime }$ and the reference clean speech, respectively. In each frame $t$ of the utterance $u$ , the overall minimum square error, $MSE$ , is obtained by comparing all the reference and inference representations of each permutation $s^{\prime }$ . The architecture for pre-training the speaker tracing module is explained in Figure 2 (c).


Reasoning Workflow	As shown in Fig. 2 , our framework accepts a pair of question and answer (QA-pair) denoted as $q$ and $a$ . It first recognizes the mentioned concepts within them respectively from the concept set $V$ of the knowledge graph. We then algorithmically construct the schema graph $g$ by finding paths between pairs of mentioned concepts (§ "Schema Graph Grounding" ). The grounded schema graph is further encoded with our proposed knowledge-aware graph network module (§ "Knowledge-Aware Graph Network" ). We first use a model-agnostic language encoder, which can either be trainable or a fixed feature extractor, to represent the QA-pair as a statement vector. The statement vector serves as an additional input to a GCN-LSTM-HPA architecture for path-based attentive graph modeling to obtain a graph vector. The graph vector is finally fed into a simple multi-layer perceptron to score this QA-pair into a scalar ranging from 0 to 1, representing the plausibility of the inference. The answer candidate with the maximum plausibility score to the same question becomes the final choice of our framework.


Results and Discussion ::: Performance on DROP's Development Set	Table TABREF24 shows the results on DROP's development set. Compared to our base models, our large models exhibit a substantial improvement across all metrics.


Sentence Selection	A summary is obliged to offer both informative and non-redundant content. While AttSum focuses on sentence ranking, it employs a simple greedy algorithm, similar to the MMR strategy  , to select summary sentences. At first, we discard sentences less than 8 words like the work of  . Then we sort the rest in descending order according to the derived ranking scores. Finally, we iteratively dequeue the top-ranked sentence, and append it to the current summary if it is non-redundant. A sentence is considered non-redundant if it contains significantly new bi-grams compared with the current summary content. We empirically set the cut-off of the new bi-gram ratio to 0.5.


Word-Level Syntactic Probes for Contextual Embeddings ::: Notation	Let $S$ be a random variable ranging over all possible sequences of words. For the sake of this paper, we assume the vocabulary $\mathcal {V}$ is finite and, thus, the values $S$ can take are in $\mathcal {V}^*$. We write $\mathbf {s}\in S$ as $\mathbf {s}= w_1 \cdots w_{|\mathbf {s}|}$ for a specific sentence, where each $w_i \in \mathcal {V}$ is a specific word in the sentence and the position $i \in \mathbb {N}^{+}$. We also define the random variable $W$ that ranges over the vocabulary $\mathcal {V}$. We define both a sentence-level random variable $S$ and a word-level random variable $W$ since each will be useful in different contexts during our exposition.


Model Description	As shown in Figure FIGREF3 , our model contains two key components, namely Truncated History-Attention (THA) and Selective Transformation Network (STN), for capturing aspect detection history and opinion summary respectively. THA and STN are built on two LSTMs that generate the initial word representations for the primary ATE task and the auxiliary opinion detection task respectively. THA is designed to integrate the information of aspect detection history into the current aspect feature to generate a new history-aware aspect representation. STN first calculates a new opinion representation conditioned on the current aspect candidate. Then, we employ a bi-linear attention network to calculate the opinion summary as the weighted sum of the new opinion representations, according to their associations with the current aspect representation. Finally, the history-aware aspect representation and the opinion summary are concatenated as features for aspect prediction of the current time step. As Recurrent Neural Networks can record the sequential information  , we employ two vanilla LSTMs to build the initial token-level contextualized representations for sequence labeling of the ATE task and the auxiliary opinion word detection task respectively. For simplicity, let INLINEFORM0 denote an LSTM unit where INLINEFORM1 is the task indicator.


Semantic Decomposition	In this study, we use GloVe  as the source algorithm for learning dense word vectors. The entire content of English Wikipedia is utilized as the corpus. In the preprocessing step, all non-alphabetic characters (punctuations, digits, etc.) are removed from the corpus and all letters are converted to lowercase. Letters coming after apostrophes are taken as separate words (she'll becomes she ll). The resulting corpus is input to the GloVe algorithm. Window size is set to 15, vector length is chosen to be 300 and minimum occurrence count is set to 20 for the words in the corpus. Default values are used for the remaining parameters. The word embedding matrix, $\mathcal {E}$ , is obtained from GloVe after limiting vocabulary to the most frequent 50,000 words in the corpus (i.e. $\mathcal {E}$ is 50,000 $\times $ 300). The GloVe algorithm is again used for the second time on the same corpus generating a second embedding space, $\mathcal {E}^2$ , to examine the effects of different initializations of the word vectors prior to training.


Knowledge Graph Representation	A knowledge graph $\mathcal {G}$ comprises of set of facts represented as triplets ( $e^s,r,e^o$ ) denoting the relationship $r$ between subject entity $e^s$ and object entity $e^o$ . Associated to this knowledge graph, we have a set of attributes that describe observed characteristics of an entity. Attributes are represented as set of key-value pairs for each entity and an attribute can have null (missing) value for an entity. We follow Open World Assumption - triplets not observed in knowledge graph are considered to be missing but not false. We assume that there are no duplicate triplets or self-loops.


RL solvers have been used to find the optimal dialog policy (e.g.,  ;  ;  ;  ). In this context, at each turn the agent acts based on its understanding of what the user said, and reward function is modeled in terms of various dimensions of the interaction such as per-interaction user satisfaction, accomplishment of the task, efficiency of interaction, and dialog duration. Recently, deep RL has also been applied to the problem of dialog management and has shown improvements over rule-based systems , , , , . In this paper, we adopt the above formalism with the goal of training a dialog policy which allows the agent to take actions that maximize its success rate (measured in terms of number of dialogs which result in launching a skill) while providing a flexible and natural way for the user to navigate throughout various dialog states. In each turn of the dialog, the agent makes its decisions based on various available information such as user's intent (e.g., asking for a particular skill), the category the user has selected, whether the user is a first-time user, etc.


Data Collection ::: Dialogue Collection ::: User Side	The user state is the same as the user goal before a conversation starts. At each turn, the user needs to 1) modify the user state according to the system response at the preceding turn, 2) select some semantic tuples in the user state, which indicates the dialogue acts, and 3) compose the utterance according to the selected semantic tuples. In addition to filling the required values and updating cross-domain informable slots with real values in the user state, the user is encouraged to modify the constraints when there is no result under such constraints. The change will also be recorded in the user state. Once the goal is completed (all the values in the user state are filled), the user can terminate the dialogue.


Implicit schedules ::: Optimization details	Scaling either the gradients $g_t$ or the per-task learning rates $\alpha $ is equivalent with standard stochastic gradient descent, but not with adaptive optimizers such as Adam , whose update rule is given in Eq. DISPLAY_FORM5. Moreover, sharing or not the optimizer accumulators (eg. running average of 1st and 2nd moment $\hat{m}_t$ and $\hat{v}_t$ of the gradients) is also impactful. Using separate optimizers and simultaneously scaling the gradients of individual tasks is ineffective. Indeed, Adam is scale-insensitive because the updates are divided by the square root of the second moment estimate $\hat{v}_t$. The opposite scenario, a shared optimizer across tasks with scaled learning rates, is also problematic as the momentum effect ($\hat{m}_t$) will blur all tasks together at every update. All experiments we present use distinct optimizers, with scaled learning rates. The converse, a shared optimizer with scaled gradients, could also potentially be employed.


Analysis of Preference	Remember that we use a preference vector INLINEFORM0 to represent the user's preference over the columns in the knowledge base. Therefore, we investigate the learned arguments grouped by profile attributes. As seen in Figure FIGREF31 , the model successfully learns the fact that young people prefer social media as their contact information, while middle-aged and elderly people prefer phone number. The result shows great potential and advantage of end-to-end models. They are capable of learning meaningful intermediate arguments while being much simpler than existing reinforcement learning methods and pipeline models for the task of personalization in dialogs.


Introduction	Social media has become a popular medium for individuals to express opinions and concerns on issues impacting their lives  ,  ,  . In countries without adequate internet infrastructure, like Uganda, communities often use phone-in talk shows on local radio stations for the same purpose. In an ongoing project by the United Nations (UN), radio-browsing systems have been developed to monitor such radio shows  ,  . These systems are actively and successfully supporting UN relief and developmental programmes. The development of such systems, however, remains dependent on the availability of transcribed speech in the target languages. This dependence has proved to be a key impediment to the rapid deployment of radio-browsing systems in new languages, since skilled annotators proficient in the target languages are hard to find, especially in crisis conditions. In a conventional keyword spotting system, where the goal is to search through a speech collection for a specified set of keywords, automatic speech recognition (ASR) is typically used to generate lattices which are then searched to predict the presence or absence of keywords  ,  . State-of-the-art ASR, however, requires large amounts of transcribed speech audio  ,  .


Model ::: Problem formulation	We formalize the problem of supervised abstractive summarization as follows. The input consists of $a$ pairs of articles and summaries: $\lbrace (X_1, Y_1), (X_2, Y_2), ..., (X_a, Y_a)\rbrace $. Each article and summary are tokenized: $X_i=(x_1,...,x_{L_i})$ and $Y_i=(y_1,...,y_{N_i})$. In abstractive summarization, the summary tokens need not be from the article. For simplicity, we will drop the data index subscript. The goal of the system is to generate summary $Y=(y_1,...,y_m)$ given the transcript $X=\lbrace x_1, ..., x_n\rbrace $.


t^{\prime }) \mid t^{\prime } \in \mathcal {E}, (h, r, t^{\prime }) \notin \mathcal {G} \rbrace $ is the set of incorrect triples generated by corrupting a correct triple $(h, r, t)\in \mathcal {G}$ .


We would compare the best performing training method to the state-of-the-art, and then do side-by-side comparison between them: Pre-large (P): To leverage the controllable attention mechanism for exploring the linguistic information in the large monolingual corpora, we create text only pre-training set by combining the first 10 million sentences of the WMT News Crawl datasets from 2007 to 2017 with 10 times M30k-half. This ends up in a large text only dataset of 10145000 unparalleled sentences in each language. P-txt: We would then pre-train our model without the image encoder on this dataset and use the M30k-half validation set for validation. P-txt-img: Once the text-only model is pre-trained, we then use it for the following fine-tuning stage on M30k-half. Except for the image encoder, we initialize our model with the pre-trained model parameters. The image encoder uses pre-trained ResNet-152  . The error gradient does not back-propagate to the original ResNet network. Scratch (S): We are also curious about the role of image can play when no pre-training is involved.


Another important aspect of the theory of definition is the distinction between essential and non-essential properties. As pointed by Burek burek2004adoption, stating that “a human is an animal” informs an essential property for a human (being an animal), but the sentence “human is civilized” does not communicate a fundamental property, but rather something that happens to be true for humans, that is, an incidental property. Analyzing a subset of the WordNet definitions to investigate their structure, we noticed that most of them loosely adhere to the classical theory of definition: with the exception of some samples of what could be called ill-formed definitions, in general they are composed by a linguistic structure that resembles the genus-differentia pattern, plus optional and variable incidental properties. Based on this analysis, we derived a set of semantic roles representing the components of a lexical definition, which are described next.


Future work may benefit from replacing ELMo with other types of contextualized representations such as BERT in our scaffold model. For example, at the time of finalizing the camera ready version of this paper,  showed that a BERT contextualized representation model  trained on scientific text can achieve promising results on the SciCite dataset.


Our approach	In this section, we will give a detailed introduction to our CRU model. Firstly, we will give a brief introduction to GRU  as preliminaries, and then three variants of our CRU model will be illustrated.


Introduction	Automatically generating captions for images, namely image captioning , , has emerged as a prominent research problem at the intersection of computer vision (CV) and natural language processing (NLP). This task is challenging as it requires to first recognize the objects in the image, the relationships between them, and finally properly organize and describe them in natural language. Inspired by the sequence-to-sequence model for machine translation, most image captioning approaches adopt an encoder-decoder paradigm, which uses a deep convolutional neural network (CNN) to encode the input image as a vectorial representation, and a recurrent neural network (RNN) based caption decoder to generate the output caption. Recently, self-attention (SA) networks, denoted as SANs, have been introduced by ,  to replace conventional RNNs in image captioning. Since its first introduction in Transformer , SA and its variants have shown promising empirical results in a wide range of CV , , , , ,  and NLP , ,  tasks. Although SAN-based framework has achieved state-of-the-art performance in image captioning, it remains two problems to be solved. Firstly, SA is susceptible to the internal covariate shift  problem.


Bi-Directional Recurrent Neural Network	 Fig. 1 shows the simplest form of the BiRNN  . Unlike its uni-directional version, the BiRNN makes use of two recurrent states, one going in the forward direction in time $\overrightarrow{\mathbf {h}}_{t}$ and another in the backward direction $\overleftarrow{\mathbf {h}}_{t}$ to model past (history) and future information respectively. The past information can be modelled by  $$\overrightarrow{\mathbf {h}}_{t} = \sigma (\mathbf { W}^{(\overrightarrow{{h}})}\overrightarrow{\mathbf {h}}_{t-1} + \mathbf { W}^{(x)}\mathbf {x}_{t})$$   (Eq. 4)  where $\mathbf {x}_{t}$ is an input feature vector at time $t$ , $\mathbf {W}^{(x)}$ is an input matrix, $\mathbf {W}^{(\overrightarrow{{h}})}$ is a history matrix and $\sigma $ is an element-wise non-linearity such as a sigmoid.


The Taskmaster Corpus ::: Two-person, spoken dataset ::: WOz platform and data pipeline	While it is beyond the scope of this work to describe the entire system in detail, there are several platform features that help illustrate how the process works.  Modality: The agents playing the assistant type their input which is in turn played to the user via text-to-speech (TTS) while the crowdsourced workers playing the user speak aloud to the assistant using their laptop and microphone. We use WebRTC to establish the audio channel. This setup creates a digital assistant-like communication style.  Conversation and user quality control: Once the task is completed, the agents tag each conversation as either successful or problematic depending on whether the session had technical glitches or user behavioral issues. We are also then able to root out problematic users based on this logging.  Agent quality control: Agents are required to login to the system which allows us to monitor performance including the number and length of each session as well as their averages.  User queuing: When there are more users trying to connect to the system than available agents, a queuing mechanism indicates their place in line and connects them automatically once they move to the front of the queue.


Introduction	Virtual assistants help users accomplish tasks including but not limited to finding flights, booking restaurants and, more recently, navigating user interfaces, by providing a natural language interface to services and APIs on the web. The recent popularity of conversational interfaces and the advent of frameworks like Actions on Google and Alexa Skills, which allow developers to easily add support for new services, has resulted in a major increase in the number of application domains and individual services that assistants need to support, following the pattern of smartphone applications. Consequently, recent work has focused on scalable dialogue systems that can handle tasks across multiple application domains. Data-driven deep learning based approaches for multi-domain modeling have shown promise, both for end-to-end and modular systems involving dialogue state tracking and policy learning. This line of work has been facilitated by the release of multi-domain dialogue corpora such as MultiWOZ , M2M  and FRAMES . However, existing datasets for multi-domain task-oriented dialogue do not sufficiently capture a number of challenges that arise with scaling virtual assistants in production. These assistants need to support a large , constantly increasing number of services over a large number of domains. In comparison, existing public datasets cover few domains.


Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition	Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.


In Danish this could be a post such as Kalle er faggot... In sub-task B the goal is to classify the type of offensive language by determining if the offensive language is targeted or not. Targeted offensive language contains insults and threats to an individual, group, or others  . Untargeted posts contain general profanity while not clearly targeting anyone  . Only posts labeled as offensive (OFF) in sub-task A are considered in this task. Each sample is annotated with one of the following labels: Targeted Insult (TIN). In English this could be a post such as @USER Please ban this cheating scum. In Danish this could be e.g. Hun skal da selv have 99 år, den smatso. Untargeted (UNT). In English this could be a post such as 2 weeks of resp done and I still don't know shit my ass still on vacation mode. In Danish this could e.g. Dumme svin... In sub-task C the goal is to classify the target of the offensive language. Only posts labeled as targeted insults (TIN) in sub-task B are considered in this task  .


While we implemented both an update-incremental and a restart-incremental RASA nlu component, the results of the two cannot be directly compared for accuracy as the underlying models differ greatly (i.e., sium is generative, whereas Tensorflow Embedding is a discriminative neural network; moreover, sium was designed to work as a reference resolution component to physical objects, not abstract intents), nor are these results conducive to an argument of update- vs. restart-incremental approaches, as the underlying architecture of the models vary greatly.


The Visual STS Dataset	STS assesses the degree to which two sentences are semantically equivalent to each other. The annotators measure the similarity among sentences, with higher scores for more similar sentences. The annotations of similarity were guided by the scale in Table TABREF4, ranging from 0 for no meaning overlap to 5 for meaning equivalence. Intermediate values reflect interpretable levels of partial overlap in meaning. In this work, we extend the STS task with images, providing visual information that models use, and assess how much visual content can contribute in a language understanding task. The input of the task now consists of two items, each comprising an image and its corresponding caption. In the same way as in STS, systems need to score the similarity of the sentences with the help of the images. Figure FIGREF1 shows an example of an instance in the dataset. In previous work reported in a non-archival workshop paper , we presented a preliminary dataset which used the text-only ground-truth similarity scores.


(Apple CEO Tim Cook introduces 2 new, larger iPhones, Smart Watch at Cupertino Flint Center event) The algorithm will output: Output: vietnam⟨O⟩Giám đốc điều hành⟨O⟩ ⟨PER⟩Tim Cook⟨PER⟩ ⟨O⟩của⟨O⟩ ⟨ORG⟩Apple⟨ORG⟩ ⟨O⟩vừa giới thiệu 2 điện thoại iPhone, đồng hồ thông minh mới, lớn hơn ở sự kiện⟨O⟩ ⟨ORG⟩Flint Center⟨ORG⟩, ⟨LOC⟩Cupertino⟨LOC⟩. With LOC, PER, ORG is Name of location, person, organization respectively. Note that O means Other (Not a Name entity). We will not denote the O label in the following examples in this article because we only care about name of entities. In this paper, we analyze common errors of the previous state-of-the-art techniques using Deep Neural Network (DNN) on VLSP Corpus. This may contribute to the later researchers the common errors from the results of these state-of-the-art models, then they can rely on to improve the model.


Our definition of bias loosely follows the idea of causal testing: matched pairs of individuals (instances) that differ in only a targeted concept (like gender) are evaluated by a model and the difference in outcomes (or scores) is interpreted as the causal influence of the concept in the scrutinized model. The definition is parametric in the scoring function and the target concept. Natural scoring functions exist for a number of neural natural language processing tasks. We instantiate the definition for two important tasks—coreference resolution and language modeling. Coreference resolution is the task of finding words and expressions referring to the same entity in a natural language text. The goal of language modeling is to model the distribution of word sequences. For neural coreference resolution models, we measure the gender coreference score disparity between gender-neutral words and gendered words like the disparity between “doctor” and “he” relative to “doctor” and “she” pictured as edge weights in Figure FIGREF2 . For language models, we measure the disparities of emission log-likelihood of gender-neutral words conditioned on gendered sentence prefixes as is shown in Figure FIGREF2 .


Conclusion and Future Work	We demonstrate that a transformer model can be trained to generate questions with correct grammar and relevancy to the context passage and answers provided. WER analyses was applied to diagnose shortcomings and guide future improvements. We observed that a low WER could be due to syntactic similarity but semantic disagreement, while two questions with syntactic divergence but similar meaning could result in a high WER. Since our results does not exhibit issues relating to contextual and syntactic roles of words within a generated question, other popular metrics (BLEU, ROUGE, F1-score, etc.) would lead to similar findings . Perhaps a better approach to evaluating question generation models is to apply state-of-the-art question answering models from SQuAD's leaderboard to measure how many answers agree. To improve the model, more and balanced data can be provided to train the model to reduce the asking word bias. One method that can be used to obtain more data is through data augmentation by back-translation . The original SQuAD can be translated into another language such as French. The translated text could then be translated back into English to generate a variation of the context, question and answers that provide more training data for the model.


Dataset and Features	We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:


Dataset ::: Russian dataset ::: Training Dataset Generation	We scraped texts from several pages about civil services from vk.com. Next we extracted frequent $n$-grams that do not contain stopwords and consist of words and digits in various combinations (such as word + 4 digits + word or word + word + 8 digits). We used several rules to merge these $n$-grams so that they resemble real hashtags, for example: remove all whitespace: wordwordworddigits Examples: ЁлкаВЗазеркалье, нескольколетназад replace all whitespace with an underscore: word_word_digits Examples: увд_юга_столицы remove some whitespace and replace other spaces with an underscore: word_worddigits. Examples: ищусвоегогероя_уфпс A word here might be a word in lower case, upper case or capitalized or an abbreviation. There might be up to four digits. In general, we introduced 11 types of hashtags, which contain simply constructed hashtags as well as the complex ones.


$  where $\delta =1$ for the top two scores and $\delta =0$ for the rest. $P_k$ is the passage created using both the facts and missing knowledge.


Though, we see that for the proper performance of the therapy is essential a person to support the user to help remember the experiences that are being asked. This project has many possible future lines. In our future work, we suggest to train the model including the Reddit dataset which could improve the chatbot model, as it has many open-domain conversations. Moreover, we would like to include speech recognition and generation, as well as real-time text translation, to make Elisabot more autonomous and open to older adults with reading and writing difficulties. Furthermore, the lack of consistency in the dialogue might be avoided by improving the architecture including information about passed conversation into the model. We also think it would be a good idea to recognize feelings from the user's answers and give a feedback according to them.


In fact, Table TABREF33 shows that the Hack Forums-trained system actually performs better on Nulled, largely due to better performance on previously-seen products. This indicates that there is some truth to Hypothesis 2: product coverage is not the only important factor determining performance.


Overall CoLA Results	The overall performance of the three sentence encoders is shown in Table TABREF33 . Performance on CoLA is measured using MCC  . We present the best single restart for each encoder, the mean over restarts for an encoder, and the result of ensembling the restarts for a given encoder, i.e. taking the majority classification for a given sentence, or the majority label of acceptable if tied. For BERT results, we exclude 5 out of the 20 restarts because they were degenerate (MCC=0). Across the board, BERT outperforms GPT, which outperforms the CoLA baseline. However, BERT and GPT are much closer in performance than they are to CoLA baseline. While ensemble performance exceeded the average for BERT and GPT, it did not outperform the best single model.


Qualitative Analysis ::: Limitations	We emphasize that QAGS and our overall framework are specifically designed to detect factual inconsistencies in generated summaries relative to the source article. QAGS does not measure other desirable properties of generated text, including fluency, readability, or factual recall. We therefore recommend using QAGS in conjunction with complementary evaluation metrics. The choices of QG and QA models in QAGS are particular to abstractive summarization and may require adaptation to be used for other conditional text generation tasks. For example, we expect that extractive summarization models may obtain nearly perfect QAGS scores because facts and statements are directly copied from the source article.


More generally, an iterated learning process is a repeated zero sum game, in which E makes predictions in virtue of some history, which one might also call a model or a set of hypotheses; if she makes a correct prediction at round n, she reinforces her beliefs in her current history; if she makes a wrong prediction, she adjusts it. The winning condition may be defined in terms of some function of the scores at each learning round or in terms of some global convergence property. Learning conceived in this way is a variant of a simple ME truth game in which costs are assigned to individual discourse moves as in discounted ME games. In an ME truth game, where E develops a history INLINEFORM0 over a set of facts INLINEFORM1 while A argues for an alternative history INLINEFORM2 over INLINEFORM3 , A can successfully defend history INLINEFORM4 as long as either the true history INLINEFORM5 is (a) not learnable or (b) not uniquely learnable. In case (a), E cannot convince the Jury that INLINEFORM6 is the right history; in case (b) A can justify INLINEFORM7 as an alternative interpretation.


Adversarial Attacks in Text	Because the purpose of adversarial attacks is to make DNNs misbehave, they can be seen as a classification problem in a broad sense. And majority of recent representative adversarial attacks in text is related to classification so that we categorize them with this feature. In this section, we introduce the majority of existing adversarial attacks in text. Technical details and corresponding comments of each attack method described below are given to make them more clearly to readers.


Results	The performances of the NER experiments are reported separately for three different parts of the system proposed. Table 6 presents the comparison of the various methods while performing NER on the bot-generated corpora and the user-generated corpora. Results shown that, in the first case, in the training set the F1 score is always greater than 97%, with a maximum of 99.65%. With both test sets performances decrease, varying between 94-97%. In the case of UGC, comparing the F1 score we can observe how performances significantly decrease. It can be considered a natural consequence of the complex nature of the users' informal language in comparison to the structured message created by the bot. In Table 7, results of the schedule matching are reported. We can observe how the quality of the linking performed by the algorithm is correlated to the choice of the three thresholds. Indeed, the Precision score increase when the time threshold decrease, admitting less candidates as entities during the matching, and when the string similarity thresholds increase, accepting only candidates with an higher degree of similarity. The behaviour of the Recall score is inverted.


We employ a two-stage training procedure, where in the first stage we use $\vec{D}_{\text{TTS}}$ to learn the initial parameters as a prior, and then use $\vec{D}_{\text{VC}}$ to adapt to the VC model in the second stage. As argued in Section SECREF1, the ability to generate fine-grained hidden representations $\vec{H}$ is the key to a good VC model, so our goal is to find a set of prior model parameters to train the final encoder $\text{Enc}^{\text{S}}_{\text{VC}}$ and decoder $\text{Dec}^{\text{S}}_{\text{VC}}$. The overall procedure is depicted in Figure FIGREF7.