Composition is constrained by the rank of a nonterminal so to ensure that at each decoding step the model is always aware of the placement of reentrant nodes.

we do not ensure semantic well-formedness in that words are predicted separately from their fragments and we do not rely on alignment information.


Early CLWE approaches required expensive parallel data (Klementiev et al., 2012; Täckström et al., 2012).

later approaches rely on high-coverage bilingual dictionaries (Gliozzo and Strapparava, 2006; Faruqui and Dyer, 2014; or smaller "seed" dictionaries (Gouws and Søgaard, 2015; Artetxe et al., 2017).


Multilingual BERT performs well on zero-shot cross-lingual transfer (Wu and Dredze, 2019; Pires et al., 2019) and its performance can be further improved by considering target-language documents through self-training (Dong and de Melo, 2019).

our approach does not require multilingual language models and sometimes outperforms multilingual BERT using a monolingual BERT student.


Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization.

most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset.


Bigpatent B also exhibits relatively higher copy rate in summary but the copy segments is shorter than CNNDM.

bigaptent b, Xsum obtain higher sentence fusion score, which suggests that the proportion of fused sentences in these two datasets are high.


2) BART (SOTA system) is superior over other abstractive models and even comparable with extractive models in terms of stiffness (ROUGE).

it is robust when transferring between datasets as it possesses high stableness (ROUGE).


Typical sources of transfer loss concern differences in features between domains (Blitzer et al., 2007;Ben-David et al., 2010).

other factors may govern model degradation for depression clas-sification.


Topical nuances in language may appropriately reflect elements of identity associated with mental health disorders (i.e. traumatic experiences, coping mechanisms)

if not contextualized during model training, this type of signal has the potential to raise several false alarms upon application to new populations.


Figure 3b shows that BiLSTM uses 35% of context for short sentences, 20% for medium, and only 10% for long sentences.

bERT leverages fixed 75% of context words regardless of the sentence length.


VQA requires techniques from both image recognition and natural language processing, and most existing works use Convolutional Neural Networks (CNNs) to extract visual features from images and Recurrent Neural Networks (RNNs) to generate textual features from questions, and then combine them to generate the final answers.

most existing VQA datasets are created in a way that is not suitable as training data for real-world applications.


VQA models following this setting take characteristics of all answer candidates like word embeddings as the input to make a selection (Sha et al., 2018; Jabri et al., 2016).

in the open-ended setting, there is neither prior knowledge nor answer candidates provided, and the model can respond with any freeform answers.


We conjecture that this is because when we have limited amount of target data, having more prior knowledge is beneficial to model performance, while having more target data will make prior knowledge less helpful.

our method can stably improve the performance because it sufficiently makes use of target data and source data.


Importantly, we do not define a precondition event as an absolute requirement for the target (the door opening) to occur in all scenarios.

we do require that the target event likely would not have occurred in the current context.


This reveals the source of improvement in attack success rate between GENETICATTACK and TEXTFOOLER to be more lenient constraint application.

gE-NETICATTACK's genetic algorithm is far more computationally expensive, requiring over 40x more model queries.


Gilmer et al. (2018) laid out a set of potential constraints for the attack space when generating adversarial examples, which are each useful in different real-world scenarios.

they did not discuss NLP attacks in particular.


Object manipulation and configuration is another subject that has been studied along with language and vision grounding (Bisk et al., 2016;Wang et al., 2016;Li et al., 2016;Bisk et al., 2018).

most studies focus on addressing the problem in relatively simple environments from a third-person view.


It is possible for instructions to be written that can pass all automated checks and still be of poor quality.

there is no quick and reliable way to automatically check if an instruction passes the tests but is still vague or misleading.


There are several benchmarks (Wen et al., 2017;El Asri et al., 2017;Eric and Manning, 2017;Wei et al., 2018) to evaluate the performance of neural models for goal-oriented dialog.

these benchmarks assume a world of a "perfect" user who always provides precise, con- cise, and correct utterances.


Zhao and Eskenazi (2018) created SimDial, which simulates spoken language phenomena, e.g. self-repair and hesitation. Sankar et al. (2019) introduce utterance-level and wordlevel perturbations on various benchmarks.

such variations have been largely artificial and do not reflect the "natural variation" commonly found in naturally occuring conversational data.


The conversational activity patterns (denoted by A) handle the main business of conversation, i.e. the user request and the services provided by agent.

conversation management patterns help the user and agent to manage the conversation itself.


Since the models are evaluated only on the agent responses present in the original test set, additional user and agent utterances for incorporating natural variation do not affect performance  Table 5: Ablation results for GLMP model on SMD too much.

sMD is a real-world dataset of human-to-human conversations collected by crowdsourcing and we observe a much higher drop across both BLEU and Ent F1 scores.


This resulted in some novelty in the data collected and prevented the user utterances to be repetitive.

to control data collection, the participants were asked to follow a set of instructions which resulted in user utterances largely focused on the task.


The dataset is the largest currently as it has largest context complexity and state complexity (based on all possible combinations of customer and agent context features, like number of flights in the database, number of airlines, airport codes and dialogue action states), in comparison to other existing datasets mentioned above.

the authors don't share details on how the dataset was collected and instructions provided to the participants


As shown in Figure 2, a correlation exists between some relevant events (such as the first joint press release) and the number of articles published.

a higher volume of articles does not always correlate with higher disagreement rates between annotators: interestingly, it seems that some events (such as the merger agreement) spread more uncertainty around the merger than others (such as the start of the antitrust trial).


Similar to our baselines ScRNN (Sakaguchi et al., 2017) and MUDE (Wang et al., 2019), Li et al. (2018) proposed a nested RNN to hierarchically encode characters to word representations, then correct each word using a nested GRU .

these previous works either only train models on natural misspellings (Sakaguchi et al., 2017) or synthetic misspellings , and only focus on denoising the input texts from orthographic perspective without leveraging the retained semantics of the noisy input.


These LMs captures the probability of a word or a sentence given their context, which plays a crucial role in correcting real-word misspellings.

all of the LMs mentioned are based on subword embeddings, such as WordPiece (Peters et al., 2018) or Byte Pair Encoding (Gage, 1994) to avoid OOV words.


XLNet (Yang et al., 2019) also marginalize over all possible factorizations.

their work is focused on the conditional distribution p(y|x), and they do not marginalize over all possible factorizations of the joint distribution.


KERMIT is a generative joint distribution model that also learns all possible factorizations.

kERMIT is constrained to two languages, while MGLM is a generative joint distribution model across any/all languages/text while learning all possible factorizations of the joint distribution.


Multilingual Neural Language Model (Wada and Iwata, 2018) uses a shared encoder and language-dependent decoders to generate word embeddings and evaluate word alignment tasks.

our work unifies the neural architecture with a straightforward stack of self-attention layers.


Our work focused on a specific instantiation of channels as languages.

mGLm is not limited to only languages and can generalize to other notions of channels.


suggest a misleading "PERSON" label 19 because of their context features, so that an incorrect NER prediction is expected if treating the three types of syntactic information equally.

the syntactic constituents give strong indication of the correct label through the word "Rights" for a "LAW" entity.


Recently, neural models play dominant roles in NER because of their effectiveness in capturing contextual information in the text without requiring to extract manually crafted features (Huang et al., 2015; Lample et al., 2016; Strubell et al., 2017; Zhang and Yang, 2018; Peters et al., 2018; Yadav and Bethard, 2018; Cetoli et al., 2018; Akbik et al., 2018, 2019; Chen et al., 2019; Devlin et al., 2019; Zhu and Wang, 2019; Liu et al., 2019b; Baevski et al., 2019; Yan et al., 2019; Xu et al., 2019a; Zhu et al., 2020; Luo et al.).

to enhance NER, it is straightforward to incorporate more knowledge to it than only modeling from contexts.


It is thus far more important to evaluate various seed configurations that various target documents.

we wanted to keep the computational cost of evaluation reasonably small, so either the number of seed configurations had to be  reduced or the number of target documents for each configuration.


In a recent edition, Rabelo et al. (2019) used a BERT model fine-tuned on a provided training set in a supervised manner, and achieved the highest F-score among all teams.

due to the reasons discussed in Section 4, their approach is not consistent with the nearest neighbor search, which is what we are aiming for.


Deep neural models have achieved impressive success in many areas.

their interpretability and explainability have remained broadly limited


Such methods extract parts of the model input that are important to the output according to some criterion.

they are not suited to evaluate NL explanations that are not part of the input, which motivates our new simulatability metric.


Among others, dependency trees help to directly link the aspect term to the syntactically related words in the sentence, thus facilitating the graph convolutional neural networks (GCN) (Kipf and Welling, 2017) to enrich the representation vectors for the aspect terms.

there are at least two major issues in these graph-based models that should be addressed to boost the performance.


These statements represent generic commonsense hypotheses about social behaviors and their acceptability that are held as norms in a society.

such normative judgments can also be strengthened or weakened given appropriate context.


Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data.

it is not clear how to integrate hyperbolic components into downstream tasks.


Many models that fuse visual and linguistic features have been proposed.

few models consider the fusion of linguistic features with multiple visual features with different sizes of receptive fields, though the proper size of the receptive field of visual features intuitively varies depending on expressions.


Zhao et al. (2018) also proposes a model with a structure that fuses multiple scales and languages for weakly supervised learning.

they use concatenation as the method of fusion, whereas we use FiLM.


The paraphrase ratio of the augmented training set remains similar as the original set.

the ratio increases in the augmented testing set indicating the paraphrase clusters are sparser in the testing set.


Another line of work learns Hornclause style reasoning rules from the KG and stores them in its parameters (Rocktaschel and Riedel, 2017; Das et al., 2018; Minervini et al., 2020).

these parametric approaches work with a fixed set of entities and it is unclear how these models will adapt to new entities.


For example, the performance (MRR) of ROTATE model (Sun et al., 2019) drops by 11 points (absolute) on WN18RR in this setting (§3.4).

we show that with new data, the performance of our model is consistent as it is able to seamlessly reason with the newly arrived data.


Recent works (Teru et al., 2020;Wang et al., 2020) learn entity independent relation representations and hence allow them to handle unseen entities.

they do not perform contextual reasoning by gathering reasoning paths from similar entities.


As a result, many of the query relations were different from what was present in the splits of NELL-995 and hence is not a good representative.

we report test results for the best hyper-parameter values that we got on this validation set.


For sentences in GENIA, the number of candidate regions generated by HiRe is 77.9% less than that of the enumeration method discarding 1.3% long entities and more than that of (Zheng et al., 2019).

the true recall of candidate regions generated by the enumeration method and HiRe are 98.7% and 98.1%, respectively.


HiRe without HRR employs Average Word Representation (denoted as AWR) instead with precision 78.3%, recall 73.7% and F1 measure 75.9%.

to HiRe AWR , the absolute F1 measure improvement of HiRe HRR is 0.6%.


Therefore some recent researches attempt to endow the bots with proactivity through external knowledge to transform the role from a listener to a speaker with a hypothesis that the speaker expresses more just like a knowledge disseminator.

along with the proactive manner introduced into a dialogue agent, an issue arises that, with too many knowledge facts to express, the agent starts to talks endlessly, and even completely ignores what the other expresses in dialogue sometimes, which greatly harms the interest of the other chatter to continue the conversation.


Models facilitated with external knowledge indeed generate more meaningful responses than peers that train only on the source-target dialogue dataset.

these models tend to fall into another situation where the machine agent talks too much ignoring what the other has said, let alone the inappropriate use of knowledge.


What's more, with copy mechanism, CopyNet, DeepCopy, and Initiative-Imitate perform better in terms of fluency and coherence because of the utilizing of proper knowledge.

comparing CopyNet and DeepCopy with Seq2Seq attn , the Engagement becomes worse because too much knowledge harms the ability to react to the proposed ques-tion very likely.


Lexical resources such as WordNet (Miller, 1995) capture such synonyms (say, tell) and hypernyms (whisper, talk), as well as antonyms, which can be used to refer to the same event when the arguments are reversed ([a] 0 beat [a] 1 , [a] 1 lose to [a] 0 ).

WordNet’s coverage is insufficient, in particular, missing contextspecific paraphrases (e.g. (hide, launder), in the context of money).


During training, each encoder learns a language model specific to an individual MT source, yielding diversity among experts in the final system.

in order to improve robustness of each encoder to translation variability, inputs to each encoder are shuffled by some tuned probability p shuffle .


Previous LSTM-based ensemble approaches propose training full parallel networks and ensemble at the final decoding step.

we found this was too expensive given the nonrecurrent Transformer model.


Most current multi-hop relation reasoning models require a good amount of training data (fact triples) for each query relation.

the relation frequency distribution in KB is usually longtail , showing that a large portion of relations only have few-shot fact triples for model training.


We look into the task of generalizing word embeddings: extrapolating a set of pre-trained word embeddings to words out of its fixed vocabulary, without extra access to contextual information (e.g. example sentences or text corpus).

the more common task of learning word embeddings, or often just word embedding, is to obtain distributed representations of words directly from large unlabeled text.


We omit the prediction time for KVQ-FH, as we found it hard to separate the actual inference time from time used for other processes such as batching and data transfer between CPU and GPU.

we believe the overall trend should be similar as for the training time.


In this field, the supervised methods, ranging from the conventional graph models (McCallum et al., 2000; Malouf, 2002; McCallum and Li, 2003; Settles, 2004) to the dominant deep neural methods (Collobert et al., 2011; Huang et al., 2015; Lample et al., 2016; Gridach, 2017; Liu et al., 2018; Zhang and Yang, 2018; Jiang et al., 2019; Gui et al., 2019), have achieved great success.

these supervised methods usually require large scale labeled data to achieve good performance, while the annotation of NER data is often laborious and time-consuming.


Then, it finetunes the model pretrained on the source task (with the output layer being replaced) using the re-annotated data to perform the target task.

it is worth noting that the NER labels of words are contextdependent.


However, given that style transfer can be viewed as a monolingual machine translation (MT) task, and that seq2seq models such as the transformer have shown to outperform unsupervised methods in multi-lingual MT when a sufficiently large parallel corpus is available (Lample et al., 2018; Artetxe et al., 2019; Subramanian et al., 2018), in our opinion it is expected that seq2seq would outperform unsupervised approaches if parallel data is available for style transfer.

to the best of our knowledge, a parallel corpus for style transfer currently does not exist.


Therefore, finding the value for STAcc is trivial once C has been found.

finding a value for C is the main issue for the metric, since it depends on evaluating the set of generated outputs how many of them were converted successfully.


We show that in a data-rich setting, with sufficient training examples, our approach outperforms a classification-based encoder-only model.

our sequence-to-sequence model appears to be far more data-efficient, significantly outperforming BERT with few training examples in a data-poor setting.


We discuss this question in Section 5.4.

as a preview, we find that the choice of target tokens has a large impact on effectiveness in some circumstances, and these experiments shed light on why T5 works well for document ranking.


While the approach can exploit pretrained knowledge when fine-tuning the latent representations, the final mapping (i.e., the fully-connected layer) needs to be learned from scratch (since it is randomly initialized).

T5 can exploit both pretrained knowledge and knowledge gleaned from fine-tuning in learning task-specific latent representations as well as the mapping to relevance decisions; specifically, we note that T5 is pretrained with tasks whose outputs are "true" and "false".


It has long been observed that most relation tuples follow syntactic regularity, and many syntactic patterns have been designed for extracting tuples, such as TEXTRUNNER (Banko et al., 2007) and ReVerb (Fader et al., 2011).

it is difficult to design high coverage syntactic patterns, although many extensions have been proposed, such as WOE (Wu and Weld, 2010), OLLIE (Mausam et al., 2012), ClausIE (Corro and Gemulla, 2013), Standford Open IE , PropS  and OpenIE4 (Mausam, 2016).


If BERT considers either name to be a common French name, then a correct answer is insufficient evidence for factual knowledge about the entity Jean Marais.

if neither Jean nor Marais are considered French, but a correct answer is given regardless, we consider it sufficient evidence of factual knowledge.


Existing approaches to improve generalization in QA either are only applicable when there exist multiple training domains (Talmor and Berant, 2019;Takahashi et al., 2019; or rely on models and ensembles with larger capacity (Longpre et al., 2019;Su et al., 2019;.

our novel debiasing approach can be applied to both single and multi-domain scenarios, and it improves the model generalization without requiring larger pre-trained language models.


Mahabadi et al. (2020) handle multiple biases jointly and show that their debiasing methods can improve the performance across datasets if they fine-tune their debiasing methods on each target dataset to adjust the debiasing parameters.

the impact of their method is unclear on generalization to unseen evaluation sets.


In addition, some works (Sukhbaatar et al., 2015;Madotto et al., 2018;Wu et al., 2019) have considered integrating KBs in a task-oriented dialogue system to generate a suitable response and have achieved promising performance.

these methods either are limited by predefined configurations or do not scale to large KBs.


However, as the KBs continue to grow in the real-world scenarios, such end-to-end methods of directly encoding and integrating whole KBs will eventually result in inefficiency and incorrect responses.

some works may put the user utterances through a semantic parser to obtain executable logical forms and apply this symbolic query to the KB to retrieve entries based on their attributes.


In the transformer, the representation of each query token gets updated by self-attending to the representations of all the query tokens and graph nodes in the previous layer.

the representation of each graph node gets updated by self-attending only to its graph neighbors according to the connections of the sparsely connected transformer as well as all query tokens.


Thus, we explore the possibility of augmenting the user-generated data with synthetic data in order to train a better model.

one needs to be careful with data augmentation using synthetic data as it inevitably has a different distribution.


However, when the size of task-related data is large enough, using a pre-trained model does not deliver much benefits (TS2 and TS all ft).

finetuning clearly improves the performance of TS2 pt in both human and automatic metrics, where using only 1k domain data already produces satisfying scores in human metrics.


Multi2 OIE yields the highest recall for all languages by approximately 20%p.

argOE has relatively high precision, but low recall negatively impacts its F1 score.


Most of the systems available for French fit in those three approaches.

none of these systems have been thoroughly compared to each other, even with the release, in 2013, of a large coreference annotated corpus (Muzerelle et al., 2014), since each system uses slightly different versions of the corpus for evaluation (e.g. different train and test sets).


CROC uses a feature indicating whether a mention is a new entity in the text, i.e. whether a mention is the first in its coreference chain.

this feature is usually only available when the corpus has been previously annotated or after the coreference resolution task: this is why we removed it.


It is difficult to study coreference errors of an end-to-end system, since it is not possible to fully separate mention misidentifications from coreference issues.

it allows a better understanding of error source.


One reason for this is that the newspapers and magazines that were used for our corpus tend to contain quite complex texts (political commentary and reports in historical German).

we also observed some systematic difficulties to apply our annotation system that is rooted in narrative theory to journalistic writing, e.g.


We generally follow this idea: reported is more summarizing and less precise, while indirect ST&WR can usually be read as a transformation of direct ST&WR that allows us to reconstruct the 'original' quote in more detail.

there are sentences that follow the typical structure of indirect ST&WRa framing clause and a dependent subordinate clause containing the content -but do not allow such a reconstruction


There is a large number of tools and software packages providing access to data repositories such as NLTK (Loper and Bird, 2002) or Spacy 1 .

many of these resources are not powerful enough to exploit this data to their full extent.


Therefore it is normally only available through a web GUI hosted by the Institute of the Czech National Corpus.

we obtained a tabular text file with the Czech-German alignment on request.


Reddit users make less use of argumentation proposition types in general: they use less normative language than the candidates and express less desire than Republican candidates.

they use reported speech often, partly because their discussions occurred after the debates had occurred.


We found that models trained through multi-task learning where the primary task consists of argument component classification and the secondary task consists of specificity classification almost always outperform models that only perform argument component classification.

the corpus used in our previous study is not publicly available and therefore our previous results are not reproducible by other members of the research community.


In our prior work (Lugini and Litman, 2018) on argument component classification for discussions, we used oversampling to alleviate the class imbalance present in argumentation labels (which is also present in the Discussion Tracker corpus).

since our Discussion Tracker experiments also include 3 task multi-task learning, oversampling with respect to argumentation labels might have negative impact on other tasks.


These hypotheses are motivated by our observation of differences between collaboration label distributions across argumentative moves.

given the different unit of analysis for the annotation of collaboration (turn) versus argumentation and specificity (argument discourse unit), for the multi-task learning setting the collaboration annotations have been converted to BIO format in order to have one annotation per argument move 2 .


Punctuation symbols often indicates segment boundaries.

there may be cases where EDUs are not segmented.


The baseline model fails to identify the comparative ‘enough . . . to' as a correlative and does not segment the sentence.

training for syntactic features allowed the model to correctly identify this construct and hence perform correct segmentation.


As suspected, the baseline model performs poorly when the sentences are longer.

formulating the problem in an alternate fashion and injecting syntax make the model perform much better.


Despite its simple mechanism, this algorithm comes with a high bias, which is unfavorable for learning new directions within the data.

multi-View-Training (Zhou and Goldman, 2004;Søgaard, 2010) tries to compensate this bias by different views of the data.


They collected explicit argument pairs with freely omissible discourse connectives which can be dropped independently of the context without changing the interpretation of the discourse relation.

sporleder and Lascarides (2008) argued training on explicit argument pairs was not a good strategy.


Note that Wu et al. (2017) collected explicit argument pairs using a similar method.

they only used argument pairs located within the same sentence while we do not apply this constraint.


We also use a content extraction tool to extract article content from an HTML file, and apply a shingling-based method to identify near-duplicate articles.


Furthermore, some datasets are available for evaluating additional dimensions of essay quality in English (Mathias and Bhattacharyya, 2018).

only a few evaluation datasets are available for Japanese writings, and even fewer Japanese learner essay datasets are.


We created the feature-based models using linguistic features based on (Lee and Hasebe, 2017).

whether these features are enough to perform AES is unclear.


As a result, the neural approach for AES has been actively studied in recent years (Taghipour and Ng, 2016).

no neural-network-based AES system is available for the Japanese language; furthermore, the BERT model has not been applied for an AES task with multiple dimensions thus far.


The feature-based model predicted a score that was two points lower than the actual content and organization trait scores in essay A and a score that was two points lower than the organization trait score in essay B.

the neural-network-based model predicted the score correctly for essay A and predicted a score that was only one point lower than the actual language trait score in essay B.


Further, this model may provide a high score for an unexpected input.

the neural-network-based model predicted low scores for all columns.


Reported results were obtained with traditional machine learning methods and, to some extent, it would be interesting to test more recent classification methods, such as deep neural networks.

the corpus might not be large enough for such an approach, which further motivates this kind of experiment.


They proved to be a strong baseline in the binary classification task, outperforming the surface text-based features and the graph-based deep semantic features.

on the five-level classification task, they were outperformed by all other feature sets.


To create such collections, there is a substantial need for automatic approaches that can distinguish the documents of interest for a collection out of the large collections (of millions in size) from Web Archiving institutions.

the patterns of the documents of interest can differ substantially from one document to another, which makes the automatic classification task very challenging.


The time-domain features offer a simple way to analyse audio signals and are directly extracted from the samples of the audio signal (waveform).

frequencydomain features are extracted from the sound spectrum, a representation of the distribution of the frequency content of sounds (Giannakopoulos and Pikrakis, 2014d).


The corpus contains tweets annotated with 28 emotions categories and captures the language used to express an emotion explicitly and implicitly.

the availability of datasets created specifically for languages other than English is very limited.


For several years, affect in speech has been encoded using discrete categories such as anger, sadness or neutral speech.

in many recent papers, researchers preferred using affective dimensions.


Regarding the effect of hesitation on fundamental frequency (f0), a study on German spontaneous speech (Mixdorff and Pfitzinger, 2005) found no impact of hesitations marked by fillers on the overall f0 pattern at the utterance level.

a study relying on synthesized speech (Carlson et al., 2006) in Swedish showed a moderate effect of the f0 slope on perceived hesitation, as well as a moderate effect of the insertion of creaky voice.


Thus all adaptations of the speaking styles to different degrees of hesitation are individual as well and cannot be summed up as a group mean.

the tendencies of the individual changes remain similar across the group.


Child language studies are crucial in improving our understanding of child well-being; especially in determining the factors that impact happiness, the sources of anxiety, techniques of emotion regulation, and the mechanisms to cope with stress.

much of this research is stymied by the lack of availability of large child-written texts.


Valence was significantly negatively associated with arousal (r = -.06, p < .001), although the effect was small, suggesting minimal collinearity.

correlations with dominance (both A-D and D-V) were much stronger and significant (p < .001).


Interpreting it at face value, we might conclude that the results reflect increased capabilities in emotion regulation (i.e., being more in control of one's emotions) (Zimmermann and Iwanski, 2014).

we are hesitant to make this conclusion because individual words likely have poor correspondence with emotion regulation, which involves complex processes.


In our results, we observe that when maximum of the 3 CCCs computed on each pair is low, the predicted satisfaction is likely to be bad.

if this maximum is high, the predicted satisfaction is likely to be good.


It often carries both positive and negative feelings.

since this label is quite infrequent, and not available in all subsets of the data, we annotated it with an additional Beauty/Joy or Sadness label to ensure annotation consistency.


The results of the crowdsourcing experiment, on the other hand, are a mixed bag as evidenced by a much sparser distribution of emotion labels.

we note that these differences can be caused by 1) the disparate training procedure for the experts and crowds, and 2) the lack of opportunities for close supervision and on-going training of the crowds, as opposed to the in-house expert annotators.


Nostalgia is still available in the gold standard (then with a second label Beauty/Joy or Sadness to keep consistency).

confusion, Boredom and Other are not available in any sub-corpus.


This line of work is predominantly based on word-level supervision.

we learn word ratings from document-level ratings.


Mean Star Rating, Binary Star Rating, and Regressions Weights learn exclusively from the available document-level gold data.

one of the major advantages of the MLFFN is that it builds on pre-trained word embeddings, thus implicitly leveraging vast amounts of unlabeled text data.


Another seemingly obvious evaluation strategy would be to predict document-level ratings from derived word-level lexica using the empathic reactions dataset in a cross-validation setup.

we found that this approach has two major drawbacks.


The clusters tend to be consistent regarding NE types, as illustrated in Table 3.

both false positives (FP; non-NE entries in NE clusters) as well as false negatives (FN; NE entries in non-NE clusters) do occur.


The transcription of the production as well as the target form are generally available in an orthographic form.

if we are interested primarily in oral production and as this oral production is sometimes restricted to isolated words, it is ultimately more important to place the analysis at the phonological level.


In this characterization, /p/ (+ coronal, + anterior) differs from /t/ (+ coronal) by one feature and similarly from /k/ (-anterior).

/t/ (+ coronal, + anterior) differs from /k/ (-coronal, -anterior) by two features, which is not very satisfactory from an articulatory point of view where it would seem logical to respect the order /ptk/, that is to say /t/ equidistant from /p/ and /k/, /p/ and /k/ being more distant.


The maximum precision score that Terrier reached is 0.92 indicating that the optimum of 1.0 was never achieved.

sODA reached the maximum score of 1.0 in five cases.


Indeed lemmatization, stemming, numeral masking and entity masking did not improve results.

stop word filtering produces better word embeddings for this task.


Currently, the serial corpora provided by NLPCC are the mainstream evaluation benchmarks for Chinese EL.

all of them stem from Chinese microblogs, which can be fairly short and noisy.


Evidently, mentions in the Hard document are rather ambiguous, as Hinrich, Chandler can refer to many different entities.

easy document contains very obvious mentions such as BRICS, UN and the country names.


These comments are useful for the learner.

comments are noise for an evaluation dataset because automatic evaluation methods utilizing corrected sentences typically rely on the matching rate between the system output and the corrected sentences to calculate a score.


In this model, we tokenized the learner sentence at the character level.

we tokenized a corrected sentence at the word level.


Therefore, it turns out that a CNN-based method is effective for errors that can be corrected with only the local context (Chollampatt and Ng, 2018).

both the NMT and SMT systems could hardly correct errors that needed to be considered in context, for example, abbreviation or formal and casual style errors.


The number of true positives (TP) in the NMT system was larger than that in the SMT system.

the number of false positives (FP) in the NMT system was considerably larger than that in the SMT system.


Lang-8's original annotation contains annotator's comments that are noise for evaluation.

our evaluation corpus does not contain such comments.


The second sentence does match the index query, so the full traversal is performed.

because there is an intervening xcomp relation, the traversal fails.


In the above example, “आतंकी हमला”  (terrorist attack) is a multiword event trigger with annotation labels B_Event and I_Event respectively.

‘हमला' (attack) itself is an event trigger with annotation label B_Event.


The recognition of medical concepts and their attributes in EEG reports is vital for many applications requiring data-driven representation of EEG-specific knowledge, including decision support systems.

the identification of the medical concepts in the EEG reports is not sufficient, as these concepts also exhibit clinically-relevant relations between them.


Since the Morphology best defines the EEG activities, we decided to use it as an anchor for each mention of an EEG activity in the EEG report.

Morphology represents the type or "form" of an EEG activity, which may have multiple values, as seen in Table 2, therefore the Morphology remains also as an attribute of the EEG activities.


In deciding the nodes of the HAD, we have consulted the Epilepsy Syndrome and Seizure Ontology (ESSO) 2 , which encodes 2,705 classes with an upper ontology targeting epilepsy and selected the concepts that best describe EEG activities.

eeG events, which are frequently mentioned in eeG reports as well, can be recognized only by identifying the text span where they are mentioned and their polarity and modality attributes.


In a controlled laboratory environment, participants used the Crowdee platform for performing the summary quality evaluation task.

to the crowdsourcing study, all the participants were also instructed in a written form following the standard practice for laboratory tests.


Automatic analysis of connected speech by natural language processing techniques is a promising direction for diagnosing cognitive impairments.

some difficulties still remain: the time required for manual narrative transcription and the decision on how transcripts should be divided into sentences for successful application of parsers used in metrics, such as Idea Density, to analyze the transcripts.


Prosodic features have been shown to be very effective to discriminate between different types of sentence boundaries and in general their usage reflects better results (Shriberg et al., 2009;Huang et al., 2014;Khomitsevich et al., 2015).

to put prosodic features into practice we need alignments between the audio and its transcription, which is hard to obtain mainly due to the low quality of the recordings.


From these results, we can assume that sequenced-figures narratives bring linguistic features also present in retellings, but the reverse direction is not true, as we can see in Table 5.

if a researcher will only work on retelling tasks, Table  5 shows that using only retelling datasets for training led to better results for the retelling task.


For a script that is not phonetic, e.g., Chinese characters, grapheme-tophoneme conversion is considered compulsory.

as Hangul is phonetic, in other words, text in Hangul sounds as it is written, we stick with graphemes rather than converting them into phonemes.


Much of the information about temples is available as text in the open web, which can be utilized to conduct such a study.

this information is not in the form of a learning resource, which can be readily used for such studies.


On the one hand high quality resources are needed that contain (English) glosses, part of speech tagging as well as underlying morphophonemes forms.

the resource needs to be large enough for the wanted forms to occur in the data.


On a positive side, 50% of the sentences were different from one seed strategy to the other, suggesting for an approach where strategies are mixed.

we also observed that (a) tends to yield more similar queries over time and (c) is too time-consuming for practical use.


When English is the source language, and Japanese is the target language, there are only 5 pairs in the test data where the source and target words are identical, i.e., cases where the copy baseline is correct.

in the case of English being the target language, and Japanese being the source language, there are 270 pairs where the source and target words are identical.


Wikipedia is usually used as a high-quality freely available multilingual corpus as compared to noisier data such as Common Crawl.

for the two languages under study, Wikipedia resulted to have too much noise: interference from other languages, text clearly written by non-native speakers, lack of diacritics and mixture of dialects.


For example, a sentence describing the English cricket team's victory over India could invoke negative sentiment, given the annotator's strong support of the latter.

the actual label of such a statement would be positive because of the author's intention.


Logistic Regression offers marginally better performance than Linear-SVM in terms of precision (Precision for LR is 0.675).

the former fails to outperform the latter in the other three metrics of evaluation.


The choice of approach has a fundamental effect on the end result: in the case of expansion (translation), the new wordnet will be fully meaning-aligned with the source language (English), which is ideal for cross-lingual uses: as most wordnets are already aligned with PWN, we get bilingual translations to all those languages 'for free'.

a certain linguistic bias is introduced by the fact that only meanings for which English lexicalisations exist will appear in the wordnet.


A common form of sarcasm consists of a positive sentiment contrasted with a negative situation (Riloff et al., 2013), therefore it was likely that learning the emotional information of a text would facilitate the task of irony/sarcasm prediction.

it was seen that the majority of Persian Twitter users include either humor, irony or sarcasm in their posts.


pre-trained a neural network model to predict emojis in the text and then transferred the model for different related tasks including sarcasm detection (Felbo et al., 2017).

with approaches that use feature engineering to extract features , in (Amir et al., 2016) features are automatically extracted by learning user embeddings which requires users' preceding messages.


Please note that we carry out our analysis on the whole corpus.

if one were interested only in the most reliable portions of the corpus, i.e. the cases all annotators agreed upon, different confidence thresholds can be set, as shown in Table 2.


The high frequency of idioms in persuasive and rhetorical language corroborates the statements by McCarthy (1998) that idioms are used for commenting on the world, rather than describing it, and Minugh 2008, who finds that idioms are used most often by those with some authority, especially when conveying 'received wisdom'.

this genre distinction is still quite crude.


Due to the fact that the majority of indigenous languages were traditionally exclusively oral cultures, manuscripts typically do not play a primary role in the study of those languages.

in particular handwritten notes that were created by researchers during fieldwork often are important information sources that, beyond other things, contain highly relevant information, ranging from object language data with attached translations and glossings, over lexical and grammatical descriptions to complex metadata, figural data and of course individual interpretation by the respective researcher.


The resulting derived resource on the one hand shows which data from the resource catalogue and which sessions from the corpora have been published in certain bibliographic items.

it demonstrates that some have actually been published in different bibliographic items.


A popular and widely method is crawling different web pages.

this is only possible under the assumption that there are sufficient web sites written in the target language.


In some cases, the large number of hapax is related to a poor quality of the corpus that might be caused by spelling errors or the presence of foreign words (Nagata et al., 2018).

our scenario is expected given the agglutinative nature of the four target languages, so they might present a vast vocabulary diversity.


Crowdsourcing platforms, such as Amazon Mechanical Turk, have been an effective method for collecting such large amounts of data.

difficulties arise when task-based dialogues require expert domain knowledge or rapid access to domain-relevant information, such as databases for tourism.


Prior crowdsourced wizarded data collections have divided the dialogue up into turns and each worker's job consists of one turn utterance generation given a static dialogue context, as in the MultiWoZ dataset (Budzianowski et al., 2018).

this can limit naturalness of the dialogues by restricting forward planning, collaboration and use of memory that humans use for complex multi-stage tasks in a shared dynamic environment/context.


The first WordNet for Bulgarian was built in the BalkaNet project (Koeva and Genov, 2004).

to this date the lexicon is not freely available.


. A free core-WordNet for Bulgarian was made available in the BulTreeBank Wordnet (Simov and Osenova, 2010), but unfortunately its size is rather small - 8 936 senses.

it has very good quality, so when we had to choose a translation for a word in English, we first looked for a corresponding synset in the BulTreeBank Wordnet.


The dictionary is compatible with GF, and contains morphology and English-Bulgarian translations.

the translations are not sense annotated.


Sense annotations are available only for the words exemplified with that sentence.

in order to provide good translations, we sense tagged all words in the corpus.


The target audience of the digital literature on this platform is young people (roughly below 30 years of age), it thus has the potential of being biased in age.

this is the age group that is the most fluent in and most likely to use written Cantonese and therefore it reflects the current usage of written Cantonese in the society.


Authors like Brysbaert and New (2009) suggest that a size of about 15 millions tokens guarantees a robust estimation of the term frequencies (i.e. which correlates well with psycholinguistic measures)

because HKC is not particularly well resourced and that corpus data (especially spoken) is not always freely available, we made do with what is available at the time, and have much less than that target size.


In order to handle these cases (which become more frequent as the resources to be modelled become more 'scholarly') we decided to make etymology a class, Etymology, in Part 3.

since etymologies usually represent an ordering of etymons (and, of course, etymons can be associated with more than one etymology and even more than one etymology for the same entry), we opted to create indirect rather than direct associations between etymologies and etymons.


If a lemma differ from one source to another, we create multiple entries and disambiguate them manually based on the definitions obtained from other resources.

if the lemma is the same we fuse their information.


We need to enrich it and validate it by Old French or diachrony specialists.

the manual process is long and tedious especially when it comes to enrich the lexicon by decreasing the silence rate.


In recent years, people have started investigating neologisms computationally (e.g. Ahmad (2000; Kerremans et al. (2011)), and online dictionaries and datasets provide convenient electronic versions of a word's year of first use.

these resources vary in the amount of information they provide and are often limited to a handful of languages.


The post-editing speed here was lower, around 1.5K words/hour.

the proportion of tags edited, 1.8%, is only slightly higher.


When given the same set of essays to evaluate and enough graded samples, AES systems tend to achieve high agreement levels with trained human raters (Taghipour and Ng, 2016).

there is a sizeable literature in cognitive science, psychology and other social studies offering evidence that biases can create situations that lead us to make decisions that project our experiences and values onto others (Baron, 2007).


Here, the target language expression is typically either a translation or a definition.

these cannot be reliably distinguished in an automated way, so that target language information is best represented as a definition rather than as a translation.


There are many existing Arabic corpora (Atwell, 2019).

we are only interested in those that include Hadith or classical Arabic text in general.


In another study, a survey was conducted to enumerate the freely available Arabic corpora and stated the existence of one Hadith corpus.

it was not accessible, mentioned or used in the literature (Zaghouani, 2017).


The number of tokens in the English Hadiths is larger than the Arabic version.

the Arabic Hadiths are richer in vocabulary as it contains more unique words than the English version as shown in Table 4.


Applications that allow users to interact with technology via spoken or written natural language are emerging in all areas, and access to language resources and open-source software libraries enables faster development for new domains and languages.

lT is highly language dependent and it takes considerable resources to develop lT for new languages.


Because of the exploratory nature of the project and the type of information that has been collected, the language actor documentation is very detailed and often shaped by the organization and work environment of the specific institution.

some good candidates for facets did emerge from the data when we analyzed it specifically with this aim in mind.


A straightforward approach would be to share the character level vocabulary between CJK languages, as it was possible between Chinese and Japanese.

this, unfortunately, is not a straightforward operation, as Hangul (the Korean writing system) is phonetic, unlike the other two examples.


The elaborate deep learning models created new standards in OCR.

like any machine learning method, deep learning models also need training material.


Like kraken, it allows the user to specify the structure of the neural network with VGSL.

to kraken, Tesseract is not GPU-enabled.


It became evident during our work that character error rates (CER) are a good indicator about the models’ ability to identifying characters correctly.

for any further data processing which may include indexing or applying text mining techniques, the bag-of-words F1-measure provides a better picture of the systems' performances.


Also, the Opus dataset is a much widely used parallel corpus resource in various researcher's works.

we observed that in both of these well-known parallel resources there are many repeated sentences, which may results into the wrong results (can be higher or lower) after dividing into train, validation, and test sets, as many of the sentences, occur both in train and test sets.


Regardless of the MT approach applied, a MT system automatically generates an equivalent version (in some target language) of an input sentence (in some source language).

despite the huge effort of the MT community, it is not possible yet to generate a perfect completely automatic translation for unrestricted domains.


Finding definite references and their antecedents in the coreference resolution data is easy.

as we described in the experiment section, it is difficult to make the correct answer rate be 50%, because most articles can be predicted using language models.


For pro-drop languages like Japanese and Chinese, zero pronoun was known to be one of the most difficult problems and many specific extensions for baseline translation methods have been discussed in previous research (Taira et al., 2012;Kudo et al., 2014;Takeno et al., 2016;Wang et al., 2016;Wang et al., 2018).

it seems that contextaware neural machine translation can handle Japanese zero pronouns just as effectively as overt pronouns in Englishto-Russian translation (Voita et al., 2018).


They built a large-scale test set from German-English bilingual texts using coreference resolution and word alignment tools.

to build a large-scale test set for Japanese zero pronouns, we have to develop accurate tools for Japanese empty category detection (zero pronoun identification) and Japanese coreference resolution, which remains open problems.


The results of the 6th WAT suggest that most sentences that are typical in TDDC and do not depend on context are translated correctly.

there are mistranslations in sentences that contain words that are not present in TDDC or whose meaning changes depending on the context.


The most comparable resource to the one presented here is the COPPA Corpus version 2 which contains around 13 million sentences.

for other language pairs our corpus is larger, e.g. for there are 6.6M English/Japanese sentences while the JW300 corpus (Agic´ and Vulic, 2019) contains around 2.1M.


As shown in Section 2.1, the standard NMT usually models a text by considering isolated sentences based on a strict assumption that the sentences in a text are independent of one another.

disregarding dependencies across sentences will negatively affect translation outputs of a text in terms of discourse properties.


MADAMIRA (Pasha et al., 2014) combines MADA (Morphological Analysis and Disambiguation of Arabic) which is built on SAMA (Standard Arabic Morphological Analyser) and AMIRA (a morphological system for colloquial Egyptian Arabic).

to MADAMIRA, FSAM's rule-based system focuses on MSA templatic morphological analysis yielding root and pattern, generation and diacritization.


On composing both FSTs, a weighted FST mapping surface form to weighted lexical forms will be generated.

if the second FST doesn't have a path for a certain analysis then the surface-form:analysis pair will be dropped.


For example, named entities, cognates/loanwords, and morphologically complex words that contain multiple morphemes are extremely challenging to properly tokenise because the occurrences of such terms are rare even in large training datasets.

substrings of such terms are likely to be more frequent.


Modern Standard Arabic (MSA), the official language of the Arab world, is well studied in NLP and has an abundance of resources including corpora and tools.

most Arabic dialects are considered under-resourced, with the exception of Egyptian Arabic (EGY).


Such models are well equipped to model some aspects of morphology implicitly as part of an end-to-end system without requiring explicit feature engineering.

these models are very data-intensive, and do not scale down well in the case of low-resource languages.


The different analyzers provide minor or no improvements over the Neural Joint Model alone when embedding the candidate tags.

the ranking approach reduces the accuracy drastically for different combinations of analyzers.


Indeed, in some languages (e.g., Basque) 100 words cannot cover even a single verb paradigm.

even in such restricted conditions some systems perform significantly better than others, the state-of-the-art approach is imitation learning via minimization of Levenshtein distance between the network output and the correct word form (Makarov and Clematide, 2018b).


They were also extensively used in Najafi et al. (2018) system, that took the second place in Sigmorphon 2018 Shared Task.

they utilized the complete Unimorph data, which is sufficiently more than 1000 word forms used in our work.


By restricting transformations to orthogonal linear mappings, VecMap and MUSE rely on the assumption that the monolingual embeddings spaces are approximately isomorphic (Barone, 2016).

it has been argued that this assumption is overly restrictive, as the isomorphism assumption is not always satisfied (Søgaard et al., 2018;.


Not further improving the results for German, Czech and Italian languages might be because of the sufficiency of the target embedding usage for domain adaptation (Jurafsky and Martin, 2014) in those.

the fact that Spanish and French performances improved on both Wikipedia and Twitter domains when Dom-Drift is used, might show that the necessity of DomDrift can be related to certain property of the target language, which can be further explored as future work.


Moreover, Turkish, Russian, Tigrigna, Polish, Uyghur, Croatian, Wolaytta, Bulgarian, German, Swedish are also characterized by high OOV rates.

mandarin, Thai, Hausa, Japanese, Vietnamese and English are characterized by low OOV rate.


Frame-semantic parsers, however, are normally trained on manually annotated resources such as the FrameNet corpus (Baker et al., 1998) or the OntoNotes corpus (Pradhan and Xue, 2009;Weischedel et al., 2013).

such annotations only exist for a small subset of the world's languages.


For both setups, we normalize labels to be conform with the PropBank (Palmer et al., 2005) notation (e.g., A1 becomes ARG1).

as shown in Figure 1, the experiments with the full label set have a slightly better accuracy than the ones with a simplified label set, so we will present only the results for the former.


Given that CS is language-dependent, a corpus for each language pair is needed.

collecting CS corpora is a very challenging task, thus the collected, and available, corpora are very scarce and cover few language pairs.


The above-mentioned differences are statistically significant.

relatively large standard deviations of the metrics should be taken into account.


More specifically, we exploit the fact that Twitter users can make use of Twitter screen names (e.g., @UserScreen-Name) in their tweet posts to mention other users, which provides us with unambiguous mentions.

e observe that many tweets also contain proper names (e.g. last names or acronyms for organizations) to refer to other Twitter users, thereby creating ambiguities about the user (entity) they refer to.


Note that we do not claim that all multimedia analysis work adopts an overly simplistic conceptualization of how text and images relate.

we find the lack of research on realistic connections between text and images is serious enough that it may hold back the state of the art in multimedia analysis for disaster management.


In sum, our analysis reveals that the image caption to some extent describes the content of the image.

in many cases the caption provides additional information which is not conveyed by the image alone.


Since the news articles in our collection are news reports, rather than editorials or feature articles, recency of what is reported in the text and depicted in the accompanying images is to be expected.

there is also a fair proportion where the image is less recent.


Up until this point, we have investigated temporal distance.

we have an important point to make about spatial distance that emerged from our manual analysis.


Success at that task could ultimately also form the basis of an automatic annotation in the future.

here we limit ourselves to a pilot, which provides a basic demonstration that the categories of news articles and images can be automatically distinguished.


Further, articles about ongoing flooding often are associated with flood-related images.

it is not advisable to assume that a flood-related image will directly relate to a flooding-event described in the corresponding article.


As in them, we constructed our questions and answers based on both textual and visual cues from short video clips.

unlike them, our proposed dataset relies on video clips that were recorded naturally by people, without predefined scripts.


This insight is in line with our previous work (Schulte im Walde et al., 2016) which also demonstrated that empirical modifier properties do not have a consistent effect on the quality of predicting compound compositionality.

ranges zooming into the prediction results for compounds with high-, mid and low-productivity heads (see Table 6), we do observe patterns for compound subsets.


This is probably one of the reasons why many studies that investigated idiomatic expressions, only collected limited information about idiom properties for very small numbers of idioms only.

this is problematic for research, because it hinders comparability of results.


On the other hand, in concrete applications, crucial domain-specific entities need to be identified in a reliable way, such as designations of legal norms and references to other legal documents (laws, ordinances, regulations, decisions, etc.).

most NER solutions operate in the general or news domain, which makes them inapplicable to the analysis of legal documents (Bourgonje et al., 2017;Rehm et al., 2017).


We hypothesize that an increase in training data would yield better results for BiLSTM-CRF but not outperform transfer learning approach of MTL (or even BioBERT).

to other common NER corpora, like CoNLL 2003 14 , even the best baseline system only achieves relatively low scores.


It connects sentiment analysis and Natural Language Generation (Zhang et al., 2018a) and facilitates a lot of NLP applications such as fighting against offensive language in social media (Santos et al., 2018), news rewriting, and building controllable dialogue systems.

this task is difficult in practice due to the lack of parallel data (sentences with similar content but different sentiments).


It suggests that the semantic representation is also essential to preserve content.

the lack of semantic representation brings little decrease in sentiment transfer accuracy.


Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research.

a big barrier to developments in this field is data access due to patient confidentiality which prohibits the sharing of this data, resulting in small, fragmented and sequestered openly available datasets.


Natural Language Processing (NLP) has enormous potential to advance many aspects of healthcare by facilitating the analysis of unstructured text (Esteva et al., 2019).

a key obstacle to the development of more powerful NLP methods in the clinical domain is a lack of accessible data.


We cannot say for certain whether using GPT-2 or EDA could positively impact our results.

it appears that our EDA baseline generally performs even worse than our Transformer and GPT-2 augmentations and the Original data itself for both MimicText-98 and MimicText-9.


It is our hypothesis that these inaccuracies can provide an optimal amount of noise when using a model that has been pretrained on biomedical texts, thus allowing them to better generalise.

this noise proves too much for models that have only been pretrained on non-medical text.


This leads us to hypothesise that this task might be too easy and that even weaker models are able to relatively accurately identify the phenotypes of patients from their discharge summaries.

we still note that our baseline models report the highest values across our metrics, especially our 'Original' data using the BioBERT model which reports the best accuracy and F1 scores.


Recently, contextualized word embeddings such as BERT (Devlin et al., 2019) have largely improved the performance of NLP tasks compared to static embeddings such as word2vec (Mikolov et al., 2013), GloVe (Pennington et al., 2014).

static embeddings are still frequently used to various studies for sentence embeddings (Yang et al., 2019;Almarwani et al., 2019) and even the other domains such as the extraction of interaction between drugs in the biomedical field (Sun et al., 2019).


These approaches effectively handle surface-variation of words such as inflected forms and typos.

this is only feasible when the roots of words are existed in a vocabulary.


The authors show that CamemBERT obtains significant improvements on many French tasks compared to the publicly available multilingual BERT.

the architecture used in CamemBERT is different to BERT, which makes the comparison among the models less straightforward.


Such results show that mean smiling intensity seems to be a more robust criterium than the sole presence or absence of smile to evaluate the impact of smiling on the success or failure of humor.

based on only four participants including one exception (MA), such a result cannot be more precise.


This result could indicate that mean smiling intensity has a larger impact of the success or failure of humor than the simple presence of smiling.

such a result cannot be considered meaningful either because, this only holds for three participants.


For example, an NMT system might translate the source sentence given in (1) as the viable Spanish translation given in (2).

in a given organizational context (3) or even (4) might instead be the approved translation.


The current implementation only uses a flat list of nonhierarchical source-target term pairs (often referred to as a "glossary") to seed the injection process, which increases the likelihood of polysemic collisions.

terminology management practices involve much more complex termbases that express several types of lexical, domain, semantic, hierarchical (taxonomic), ontological, overlapping, and nesting relationships among terms.


Overall, the percentage of rare words gets smaller as corpus size increases, as more and more words appear over 10 times.

the hyperparameters seem to have different effects on this value depending on corpus size as well.


The naming form distribution in the GTTC does not reflect the natural distribution of naming forms because we oversampled Dr.-containing tweets.

the relation between naming and stance should not be affected by oversampling.


Finally, the political orientation of users was determined by a proxy that assumes that if you criticise right-wing politicians you are more likely to be left-leaning and vice versa.

of course, you can also criticise politicians from your own political spectrum.


A sample of titlecontaining tweets suggests that titles are not a definite signal that an otherwise positive-sounding German tweet is meant to be interpreted negatively (or vica versa).

since the use of honorifics can be indicative of sarcasm (Liu et al., 2014), it is worth investigating whether the use of titles alongside explicit negative stance should be interpreted as sarcasm, and whether this sarcastic use plays a role in causing the weaker positive association with formal naming in left-leaning discourse.


A typical application of classical SA in an industrial setting would be to classify a document like a product review into positive, negative or neutral sentiment polarity.

to SA, the more fine-grained task of Aspect-Based Sentiment Analysis (ABSA) (Hu and Liu, 2004;Pontiki et al., 2015) aims to find both the aspect of an entity like a restaurant, and the sentiment associated with this aspect.


As we mentioned above, majority works have boiled down opinion mining to a problem of classifying whether a piece of text expresses positive or negative evaluation.

evaluation is, in fact, much more complex and multifaceted, which varies depending on linguistic factors, as well as participants of the communicative activity.


Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity.

other shifters are restricted to a single shifting direction.


The dataset created in (Castro et al., 2018) was used by Castro et al. (2018) in the context of the HAHA 2018 competition for Humor Detection and Funniness Average Prediction

the dataset presented in (Castro et al., 2018) still presents some issues.


None of the systems could beat this baseline in terms of precision.

the recall of this baseline is very low, because many humorous tweets are not written as dialogues, and that is why its F1 score is not that high.


For a further development of this line of work, it is essential to construct a linguistically valid treebank on CG.

current corpora based on CG often do not take advantage of linguistically adequate analyses developed in the CG literature, mainly because these corpora are converted from existing resources which do not contain fine-grained annotation (Honnibal et al., 2010).


Information about a predicate's arguments are encoded only indirectly and their immediate accessibility depends on the precise type of PS treebank.

dependency structures (DS) abstract away from linear order and concentrate on encoding functional dependencies between the items of a clause.


One can include functional labels representing grammatical relations in a PS parser.

it has been shown that training a PS parser by including functional labels produces lower constituency parsing accuracy.


It includes an Urdu text corpus of 1.6 million words and a parallel English-Urdu corpus containing 200K words.

the Urdu EMILLE copora are unannotated with respect to grammatical structure.


It seems that the main takeaway is "the more, the better," as the top-scoring setup uses all five auxiliary treebanks.

we get a significantly stronger improvement from the constituency treebanks than from the dependency treebanks.


For language-specific models and questions, such representations are often adequate and may even be preferable to the alternatives.

in multilingual models, the language-specific nature of phonemic abstractions can be a liability.


The usefulness of AlloVera for all purposes will increase as it grows to cover a broad range of the languages for which phonetic and phonological descriptions have been completed.

to illustrate the usefulness of AlloVera, we will rely primarily on the zero-shot, universal ASR use-case in the evaluation in this paper.


Then phonetic representations could be derived by first applying G2P to the orthographic text, then applying the appropriate transducer to the resulting phonemic representation.

constructing such a resource is expensive, requires several specialized skills on the part of curatorswho must encode the phonological environments in which allophones occur-and requires information that is often omitted from phonological descriptions of languages.


Therefore, adding more languages can confuse the model, leading it to assign incorrect phonemes.

alloVera provides a consistent assignment across languages by using allophone inventories.


The universal phone inventory consists of all allophones in AlloVera.

the shared phoneme model could only generate inconsistent universal phonemes and the private phoneme model could only generate languagespecific phonemes.


The term allophone was coined by Benjamin Lee Whorf in the 1920s and was popularized by Trager and Block (1941).

the idea goes back much further, to Baudoin de Courtenay (1894).


The row tagged with Full means that the whole training set was used to train the multilingual model.

the row with tag Low is trained under a low resource condition in which we only select 10k utterances from each training corpus.


The recognition and automatic annotation of temporal expressions (e.g. Add an event for tomorrow evening at eight to my calendar) is a key module for AI voice assistants, in order to allow them to interact with apps (for example, a calendar app).

in the NLP literature, research on temporal expressions has focused mostly on data from the news, from the clinical domain, and from social media.


Snips (Coucke et al., 2018) 5 is a crowdsourced dataset for the voice assistant domain, specifically for seven intents 6 , which is widely used for benchmarking NLU components of voice assistants.

no explicit details are provided on how the data was created or collected, and it does not appear to come from a real-world interaction with a voice assistant (sentences from Snips can at times be rather odd, albeit grammatical, e.g.


An hour is then annotated as a DURATION.

the reservation needs to be done for a specific punctual time that is not expressed here.


Similar to Wang et al. 2019, on the one hand we annotate the reliance on factual knowledge, that is (Geo)political/Legal, Cultural/Historic, Technical/Scientific and Other Domain Specific knowledge about the world that can be expressed as a set of facts.

we denote Intuitive knowledge requirements, which is challenging to express as a set of facts, such as the knowledge that a parenthetic numerical expression next to a person's name in a biography usually denotes his life span.


For the non-BERT models, DocQA utilizes the loss value from the answer span prediction to check answerability, while Read+Verifier introduces a new classifier for verifying the question and answer pair.

bERTbased models first pretrain deep bidirectional representations from large-scale unlabeled text without any explicit modeling for a specific task.


These previous studies have mostly focused on factoid questions, each of which can be answered in a few words or phrases generated by understanding multimodal contents in a short video clip.

this problem definition of video question answering causes some practical limitations for the following reasons.


Beyond these functionalities, which can in principle be achieved with large finite-state dialogue models, commercial assistants were not intended to support extended conversations on topics in the general domain, although their capacities for chitchat have been gradually increasing (Fang et al., 2018).

the systems known as chatbots were intended from the start to offer robust conversational capacities, although with a thin and often implicit knowledge base.


Given the outstanding recent results of the BERT model applied to QA on the SQuAD dataset, we selected this model for our QA component.

the model is designed for answer extraction from a specific paragraph -with provision for the cases when the paragraph does not contain the answer -while we need to develop an end-to-end solution, starting directly from the document repository, without prior knowledge of the paragraphs relevant to each question.


These analyses confirm the possibility to use syntactic n-gram features in cross-lingual experiments to categorize texts according to their CEFR level (Common European Framework of Reference for Languages).

text length and some classical indexes of readability are much more effective in the monolingual and the multilingual experiments than what Vajjala and Rama concluded and are even the best performing features when the cross-lingual task is seen as a regression problem.


Most of the works in this area have been focused on English as a second language where the needs are the most obvious (Condon, 2013;Weigle, 2013).

although knowing a lingua franca is important, being able to integrate oneself into another culture by communicating in its language is also important.


In this experiment, the texts written in German are used to predict separately the CEFR level of the Italian and Czech texts.

as shown in Table 1, there are very large disparities between the three languages regarding the number of texts classified in each CEFR level.


The analyses support their conclusion that it is possible to use features like POStag or dependency ngrams to learn a predictive model on one language and then use it to categorize texts written in another language.

the complementary analyses suggest that the number of words in each text and some classical indexes of readability are more effective when the task is seen as a regression problem.


It follows that making the code available in the form of a Docker image as it was required for REPROLANG 2020 is useful in order to ensure that the results can be reproduced, but it does not guarantee that these results correspond to the explanations given in the paper.

providing a Docker image makes it possible to find the versions of the programs and modules used, often necessary for reproducing exactly a study.


These patterns are helpful to find irony samples in a given corpus.

they cannot be used as irony detection algorithms due to their limited coverage.


We initially attempted to set this up as a labeling task by providing participants labels and definitions.

we found it difficult for annotators to pinpoint differences between versions where edits modify or provide new information, in contrast to providing only stylistic changes.


They are considered improvements over word models, and their effectiveness is usually judged with benchmarks such as semantic similarity datasets.

most of these datasets are not designed for evaluating sense embeddings.


As mentioned in Section 3.2, GenSense-1 and SenseRetro-1, in which only first sense vectors are utilized, outperform their multi-sense counterparts in MEN, RW, WS353, and SCWS.

on MSD-1030 an opposite pattern is shown.


Thus, semantic attribute vectors can effectively capture the commonalities and differences among concepts.

as semantic attributes have been generally created by psychological experimental settings involving human annotators, an automatic method to create or extend such resources is highly demanded in terms of language resource development and maintenance.


Interestingly, the predicted attributes (originated from visual attributes annotated in ViSA) contributed to the performance improvement in VisSim and SemSim, outperforming not only the existing results but also that with fast-Text.

significant differences were not observed between the dir and ext tasks.


So far, brain research has offered insight on the fact that we develop mental images for words learned.

what this fails to tell us is how they would look like visually.


Abstract shapes contain descriptions like "rectangle" for Turkey or "pointy" for Somalia.

associations use other objects that look similar to the shape of the country as a reference.


This shows that people's size description generally matches the actual size of the country on the world map.

the location of the country seems to play an important role in the accuracy of people's descriptions.


These datasets allow modern machine learning techniques to glean insight from the massive amounts of textual data they contain.

in the areas of humor classification and generation we find much smaller datasets, due to the complexity of humorous natural language.


A task similar to that of (Hossain et al., 2019;Weller and Seppi, 2019) can be done with this dataset, where a model predicts the level of humor found in the joke in order to examine what characterizes humor.

due to Reddit's large scale and uneven distribution of upvotes, predicting the number of upvotes would be a sparse and difficult task.


Many studies have been proposed in recent years to deal with online abuse, where swear words have an important role, providing a signal to spot abusive content.

as we can expect observing the different facets of swearing in social environments, the presence of swear words could also lead to false positives when they occur in a nonabusive context.


Zannettou et al. (2018) compared the behavior of troll accounts with a random set of Twitter users to analyze the influence of troll accounts on social media.

new accounts can be opened at any time, and troll accounts can be suspended or deleted at any time.


As such, we can conclude that these selected stylometric features can be successfully transferred from one language to another.

most of the stylometric features are language-dependent and will also rely on external natural language processing techniques.


In tweet (7), the writer uses a joke to make an ironic statement about the social problem of the reluctance of young men to marry.

in example (8) and despite the use of the hashtag ("#irony"), the tweet is just a simple joke about a girl mosquito.


The only previous attempt at normalizing Italian social media data is from Weber and Zhekova (2016).

they have a different scope of the task, mostly focusing on readability, not on normalization on the lexical level.


However, perhaps surprisingly, when training on canonical data (ISDT), using predicted normalization on the input data leads to a slightly better performance compared to using gold.

the differences are very minor in this setting, and considering the size of the test data (100 tweets), we can not draw any conclusions from these results.


Prior studies have analyzed how location affects the type of language that people use, often looking at text written by authors from different countries when exploring crosscultural differences (Poblete et al., 2011; Garcia-Gavilanes et al., 2013).

it is not always necessary to look at multiple countries in order to view different cultures.


In most cases, the speech is clearly pronounced, well articulated and easy to understand.

oral history interviews are often recorded using conventional recording equipment that was common at the time of recording.


This setup also achieves slightly better results than the proposed approach on the Challenging Broadcast test set with the larger language model.


After launching the procedure of annotation, Analor creates another TextGrid file for every sound file containing a new tier of automatically segmented periods.

analor creates only one tier with periods but TextGrid files of manual annotation contain a tier for each speaker in every sound file.


Works like Tacotron (Wang et al., 2017),Tacotron 2 (Shen et al., 2017), Deep Voice 3 (Ping et al., 2017) are capable of producing high quality natural speech.

all of these methods are data hungry and require approximately 24 hrs of text-to-speech data for a single speaker.


This corpus was used to train text-to-speech systems for 13 languages were developed in (Pradhan et al., 2015).

in this corpus, the amount of data provided per language is far too less (≈25% of recent TTS datasets) for training recent neural network based systems that can produce natural, accurate speech.


It can also be seen that the alignment curve shape is inferior in the case of Malayalam, and this is also reflected in the lower MOS scores for Malayalam.

hindi and Bengali has near perfect alignment curves which corresponds to the higher MOS scores that we get for these languages.


Using a limited database of Mizo tones, the authors reported that pitch height and F0 slope can automatically classify Mizo tones into the four phonological categories with considerable accuracy of 70.28%.

this work had several shortcomings, firstly, the Mizo database used for the work was considerably small; secondly, the approach for identifying tones was threshold based and no statistical method was incorporated.


Each tone combination consists of five unique phrases which were recorded three times by each speaker which outcome is 17, 280 phrases resulted in 54, 720 total tokens (19 speakers x 64 tonal combinations x 5 trisyllabic phrases x 3 monosyllables x 3 repetitions).

22, 770 tokens are not considered as these are the low tones derived from RTS which is not considered in the present work.


The design of our corpus is based on S-JNAS, so we also used the ATR 503 sentences and JNAS newspaper article sentences as the script for our participants.

unlike S-JNAS, we used the ATR 503 sentences as training data and the newspaper article sentences as test data.


For the S-JNAS corpus, each of the training data speakers read aloud two sets of ATR 503 sentences (about 100 sentences) and one set of the newspaper article sentences (about 100 sentences).

for our corpus, since many of our speakers are very elderly, and some have limited vision or a tendency towards dementia, we limited the number of sentenced we asked each participant to read in order to reduce the burden.


Unlike in the other areas, there was insufficient coaching of the Tokushima participants by the recording staff, such as prompting them to read the text more carefully when they made mistakes, or having them re-read the text aloud when they made serious errors.

speech from the Yamagata speakers obtained the best recognition results, and this may have been because the average age of the participants was the lowest, at 73.4 years, which may have helped them to read aloud more fluently.


The result shows that when dealing with American read material, the word error rate (WER) was 3.1% and when dealing with American/Canadian spontaneous speech, WER was 7.94%.

when the system was used to transcribe IE, WER was much higher and was 22.89%.


In general, the scores on the manual pyramids are higher than on automatic pyramids as the average scores in table 5 show.

the high Pearson's correlation between quality scores on manual and automatic pyramids, especially when we use emb 2m, leads us to argue that this could be an issue of coverage.


Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI.

such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet.


In more extreme cases, the claim starts with a pronoun.

it may be necessary to know that before determining whether the claim is supported or not, because there may be multiple lines in the original evidence source that could be talked about.


One way to generate refuted claims is to perform automatic claim negation using rulebased 'not' insertion based on syntax and part-of-speech (Bilu et al., 2015).

this would result in a handful of negation words appearing in the refuted claims by design, causing classifiers to exploit this pattern.


Another automated approach is to pick a different random claim from the dataset.

a "refuted" claim chosen this way is likely to be topically dissimilar from the evidence file, rendering it not a useful negative example for the classifier.


The crucial differentiating factor, then, might be the existence of antonyms: a claim is more likely to be refuted by e c if it contains even one antonym of a word in e c .

a claim c that is supported by e c should tend to have no antonyms.


Some such data sets which have enabled the advancement of NLI (and fact verification) are SNLI (Bowman et al., 2015) MNLI (Williams et al., 2017), FEVER (Thorne et al., 2018), and FNC (Pomerleau and Rao, 2017).

these datasets are not devoid of biases (subtle statistical patterns in a dataset, which could have been introduced either due to the methodology of data collection or due to an inherent social bias).


For example, in the second data point the bias of Clinton towards the label Agree (i.e., the percentage of data points where the entity Clinton cooccurred with the label Agree) is 63.15%.

the model trained on delexicalized data was able to predict the label with a lower bias (Disagree with 36.85%).


In Thai, spaces are used to separate sentences.

they are used for other purposes as well, such as separating phrases, clauses, and listed items.


To address this problem, ideally, the corpus needs to be extended to cover the target domain.

this usually comes with high costs and requires time, so it is often not feasible.


We suppose that the language model used in this study, which is trained mainly on hotel reviews, could be the most beneficial for segmenting user-generated data in the hotel domain.

there is no annotated corpus for the hotel domain publicly available at the current time.


Since for uttering responses with the high concreteness like "response 6" it is necessary to deeply consider the content of narratives, the degree of empathy shown by these responses tends to be high.

since it is not necessary to deeply consider the content of narratives for uttering responses with low concreteness like "response 5," the degree of empathy shown by these responses tends to be low.


Since responses of high versatility are uttered at various points in narrative speech, it is considered that these responses occur along with many other types of responses.

since responses of low versatility are uttered in fewer points in narrative speech, it is considered that these responses do not occur along with many other types of responses.


Empathy to narratives encourages a speaker to speak more only when the degree of the empathy is appropriate.

when the degree of the empathy is not appropriate for the narrative, such empathy discourages the speaker.


It offers flexibility to choose the type of annotation and labels as well as several other options during the annotation (e.g., sentence marking, break line and white space deletion).

it does not support multiple parallel annotators nor it is deployed in a server, thus, lacking the ability to track the annotator's progress and to flexibly work on different machines.


Doccano also allows for the setting of task-specific labels.

only categorical labels are supported and the customization of these is also limited to annotation tasks with similar label requirements such as NER, sentiment analysis, and translation.


AWOCATo includes a customizable guideline page in HTML.

to cater to users with limited HTML knowledge, the annotation guidelines can be created, for example, in Google Docs 13 , exported as HTML and stored in a predefined folder.


This complexity in the code is the artifact of supporting a very large number of features that are needed in certain cases.

there are cases where all these features might not be necessary, for instance, researcher who are new to sequence-to-sequence (seq2seq) modeling might need more simpler codes to start with.


ESTNLTK library is an extendable collection of NLP utilities which use Text objects to communicate with each other.

practice showed that the original structure of Text objects was not easily extendable and we had to rethink how the information is stored and structured.


Evaluation data was initially taken from the Estonian National Corpus (ENC) (Kallas and Koppel, 2018), which is the largest published collection of Estonian texts so far.

we discovered errors in one of its subcorpora.


More generic specifications for component metadata are currently developed in coordination with Teanga development, and will be partially based on the Fintan ontology.

we plan to align our specifications also with those of the European Language Grid (ELG), 27 and thus anticipate a longer consolidation process and several cycles of revision until we arrive at stable specifications.


Ideally these data should record interactions between real users and a dialogue system, or, if a dialogue system is not available (which is very common in the initial stages of development), interactions between real users and a Wizard (a human playing the role of the system), in a so called Wizard of Oz (WOz) setting (DahlbÃƒÂ¤ck et al., 1993).

this approach can be quite expensive and time consuming.


Also, to take into account the fact that SU actions are generated based on a probability distribution, expected precision, expected recall, and expected accuracy are used (Georgila et al., 2006).

these metrics can be problematic because if a SU action is not the same as the user action in the reference corpus, this does not necessarily mean that it is a poor action.


Detecting and interpreting the temporal patterns of gaze behaviour cues is natural for humans and also mostly an unconscious process.

these cues are difficult for conversational agents such as robots or avatars to process or generate.


The Wikisource contains the full text of the Twenty-Four Histories under the Creative Commons license, i.e., they could be freely used, re-distributed, and modified.

there are two limitations: the philological provenance and the current format.


In this paper, we look to characterize phonotactics at the language level.

we use methods more typically applied to specific sentences in a language, for example in the service of psycholinguistic experiments.


We conclude that /x/ is in the consonant inventory of at least some native English speakers.

counting it on equal status with the far more common /k/ when determining complexity seems incorrect.


Like final obstruent devoicing, vowel harmony plays a role in reducing the number of licit syllables.

to final obstruent devoicing, however, vowel harmony acts cross-syllabically.


QDMR abstracts away the context needed to answer the question, allowing in principle to query multiple sources for the same question.

to semantic parsing, QDMR operations are expressed through natural language, facilitating annotation at scale by non-experts.


QDMR is primarily inspired by SQL (Codd, 1970;Chamberlin and Boyce, 1974).

while SQL was designed for relational databases, QDMR also aims to capture the meaning of questions over unstructured sources such as text and images.


We see compression in both types of contexts, which suggests that the cognitive load hypothesis is the more likely account.

these two hypotheses are not mutually exclusive.


In these methods, syntactic-guidance is sourced from a separate exemplar sentence.

this prior work has only utilized limited syntactic information available in the parse tree of the exemplar sentence.


Our task is similar in spirit to Iyyer et al. (2018) and Chen et al. (2019a), which also deals with the task of syntactic paraphrase generation.

the approach taken by them is different from ours in at least two aspects.


Recent studies (e.g., Linzen et al., 2016; Marvin and Linzen, 2018; have explored this question by evaluating LMs' preferences between minimal pairs of sentences differing in grammatical acceptability, as in Example 1.

each of these studies uses a different set of metrics, and focuses on a small set of linguistic paradigms, severely limiting any possible bigpicture conclusions.


Marvin and Linzen (2018) expand the investigation to negative polarity item and reflexive licensing.

these and related studies cover a limited set of phenomena, to the exclusion of well-studied phenomena in linguistics such as control and raising, ellipsis, quantification, and countless others.


in the simple LM method, suggesting that the probabilities Transformer-XL assigns to the irrelevant part at the end of the sentence very often overturn the observed preference based on probability up to the critical word.

gPT-2 benefits from reading the whole sentence for BINDINg phenomena, as its performance is better in the simple LM method than in the prefix method.


Our benchmarks are exact reproducible in the sense that we provide the tables that record all model results (Section 3.3) and the code to run and evaluate our HPO algorithms (Section 6).

they are not guaranteed to be broad reproducible, because the generalizability of the results might be restricted due to fixed collections of hyperparameter configurations, the variance associated with multiple runs, and the unknown best representative set of MT data.


The preceding discussion shows that if we have a set of terminals that are anchors for the true nonterminals in the original grammar, then the productions and the (bottom-up) parameters of the associated productions will be fixed correctly, but it says nothing about parameters that might be associated to productions that use other nonterminals.

it is easy to show that under these assumptions there can be no other nonterminals.


One strand of research looks at using the IO algorithm to train some heuristically initialized grammar (Baker, 1979;Lari and Young, 1990;Pereira and Schabes, 1992;de Marcken, 1999).

this approach is only guaranteed to converge to a local maximum of the likelihood, and does not work well in practice.


For example, if Bob is a bakeoff organizer, he might want accuracy above 60% in order to determine whether to manually check the submission.

if Bob is providing ''MT as a service'' with strong privacy guarantees, he may need to provide the client with accuracy higher than 90%.


Equipped with external knowledge and multi-task learning, our model can further reduce chaotic logic and meanwhile avoid repetition.

the analysis result illustrates that generating a coherent and reasonable story is challenging.


But the model does not succeed in the style transfer task, and simply learns to add the word doctors into layman sentences while almost keeping the other words unchanged; and adding the word eg into the expertise sentences.

it achieves good performance on all of the three ST measures, but makes little useful modifications.


Moreover, these two structure encoders are bidirectionally calculated, allowing them to capture label correlation information in both top-down and bottom-up manners.

hiAGM is more robust than previous top-down models and is able to alleviate the problems caused by exposure bias and imbalanced data.


Note that DAG can be converted into a tree-like structure by distinguishing each label node as a single-path node.

the taxonomic hierarchy can be simplified as a tree-like structure.


The key information pertaining to text classification could be extracted from the beginning statements.

we set the maximum length of token inputs as 256.


Regardless of their source, emails are usually unstructured and difficult to process even for human readers (Sobotta, 2016).

many approaches have been proposed for cleansing newsgroup and email data.


Importantly, dependency paths between content words do not generally contain function words.

by comparing paths across languages, differences in the surface realization are often masked, and argument structure and linkage differences emphasized.


Japanese and Korean are largely similar from the point of view of language typology (SOV word order, topic prominence, agglutinative morphology), but there are also important differences on the level of usage.

the adjective class in Korean is less productive, and translations often resort to relative clauses for the purposes of nominal modification.


The "most common other path" for both Russian and French is xcomp+nsubj, which is easy to explain: PUD corpora of these languages "demote" fewer auxiliary predicates than English (criteria for demotion are formulated in terms of superficial syntax and differ between languages) and more often place the dependent predicates as the root.

in constructions like he could do something the direct edge between the subject and the verb of the dependent clause is replaced with two edges going through the modal predicate.