It is remarkable that in both languages the most offensive tweets were associated with the Venezuelan political incident.

the same can happen with the surprise emotion.


Some of the most commonly used datasets in recent studies are listed below.

anger, disgust and fear were more usual for the Venezuela situation.


The tool is mainly divided into two parts.

emotion carriers extraction, for example, could be a useful task for conversational mental healthcare applications.


In this work, we present our experiments with manual annotation of emotion carriers in spoken PNs in the German language from the Ulm State-of-Mind in Speech (USoMS) corpus (Schuller et al., 2018).

in this task, the dataset is provided with the sentiment (positive or negative) of the text, and our goal is to find the emotion carriers for that sentiment.


Performances are lower on the LargeSet (wQBC = 1.67) than on the SmallSet (wQBC = 1.62).

other than the anticipation of linguistic elements, hesitation might be provoked by uncertainty of the communicated content.


This result is not surprising but could explain certain biases in the data set.

to enable their comparison between chunks with various segmental contents, formant values in Bark scale were converted to distances to centroids.


Table 3 shows the average VAD and EI scores of poems in PoKi and poems written by adults.

we also know that emotion language shifts over development from a focus on subjective feelings to external features (O' Kearney and Dadds, 2004).


The present work is the first step of a bigger project located at the cross-domain between semantic and paralinguistic information modeling in call center spontaneous speech directly from audio signal.

in order to calibrate the perception of the satisfaction dimension, we extracted from the corpus two conversations that we thought were the extreme boundaries of frustration and satisfaction i.e. the conversations with the most frustrated caller and another one with the most satisfied caller.


in order to calibrate the perception of the satisfaction dimension, we extracted from the corpus two conversations that we thought were the extreme boundaries of frustration and satisfaction i.e. the conversations with the most frustrated caller and another one with the most satisfied caller.

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


This annotation procedure relaxes the reader perspective, as we encourage annotators (if in doubt) to annotate how they think the other annotators would annotate.

even though the crowds' labels look inconsistent at first view, there appears to be a good signal in their aggregated annotations, helping to approximate expert annotations to a certain degree.


Those ratings are used (2) to train sentiment-aware word embeddings.

based on a Twitter corpus with hashtag-derived polarity labels, they (1) apply the method of Mohammad (2012) to generate a first set of word labels (see above).


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.

this gives lexica an important role for building justifiable AI and addressing related ethical challenges (Clos et al., 2017).


In light of this development, lexica, lists of words and associated weights for a particular affective variable, which used to be a key component for feature extraction (Mohammad and Bravo-Marquez, 2017a), may seem obsolete.

lowempathy words, on the other hand, are often ones used for ridiculing, hence expressing a lack of empathy (joke, wacky).


Furthermore, Lores (2004) investigated the usage of Theme and Rheme in abstracts of scholarly articles.

academic Phrasebank (Morley, 2014) is one of them.


The communicative function of the targeted sentence is also shown.

we note that 64.7% of the data showed 100% accuracy, and the accuracy for 84.4% of the data is greater than 75%, which implies that the majority of the quizzes are easy to answer.


Moreover, a few attempts have been made to create a list of formulaic expressions (Simpson-Vlach and Ellis, 2010; Ackermann and Chen, 2013).

it cannot be used as a ground-truth dataset.


Formulaic expressions are extracted from the example expressions by hand, but because they can be very specific or sometimes contain irrelevant content, some queries return no results.

a low accuracy and high agreement indicate that the dataset is of low quality.


A building block of characteristic metrics for text collections is the language representation method.

with more outliers or more sub-clusters, the diversity metric can also reflect the increasing dispersion of cluster distributions but is less sensitive in high-dimensional spaces.


To accommodate the impact of high-dimensionality, we impose a dimension normalization.

when there are two or more class labels in a text collection, in an ideal scenario, we would expect the homogeneity to be monotonically decreasing.


We down-sampled SST-2 and Snips training sets from 100% to 10% with intervals being 10%.

how uniformly the embedding vectors of the samples in a group of texts are distributed in the embedding space.


Event Mention Retrieval (EMR) task: given a query consists of a handful of event mentions, return all relevant event mentions in a corpus.

the ad-hoc retrieval model does not increase when k increases.


In this paper, we provide an adapted benchmark data set that is based on a test collection originally used to evaluate information retrieval systems.

note that there are several different database schema designs already available if one uses the internet movie database directly as a data source.


At the same time, (Chandran and Kasturi, 1993) designed a structural table detection method based on horizontal and vertical lines, as well as the item blocks.

their recognition process differs significantly from previous approaches as it realizes a bottom-up clustering of given word segments, whereas conventional table structure recognizers all rely on the detection of some separators such as delineation or significant white space to analyze a page from the top-down.


(3) Using weak supervision that consists in pre-training a supervised model on data labelled with an unsupervised approach (Dehghani et al., 2017).

for example, if we consider the query "Developmental disorder", the most relevant (relevance = 2) document is "Developmental disorders comprise a group of .


The annotated corpus is split into training, development, and test sets.

to compute mention embeddings, CONtES uses word embeddings from a large corpus.


Habitat mentions are subject to much more variations, and, due to the microscopic nature of bacteria, any object or place can be construed as a habitat.

we set a short symmetrical window of two tokens in all subsequent experiments.


To the best of our knowledge, this is among the first publiclyavailable Chinese EL dataset derived from long text and annotated with two major Chinese KBs (Chinese Wikipedia and CN-DBpeida).

we are not aware of any direct research in characterizing EL corpus in terms of difficulty.


The evaluation results on CLEEK and the subsets divided according to difficulty metric reveal that our proposed corpus is of high quality with documents in different levels of difficulty, and the difficulty measure can well characterize the ambiguity of documents.

far, numerous English EL corpora have been constructed from different types of sources, including news (Hoffart et al., 2011;Cucerzan, 2007;Rosales-Méndez et al., 2018), tweets (Rowe et al., 2014) and RSS feeds (Röder et al., 2014).


Bridging corpus linguistics and neuroimaging methodology may open new perspectives in language research.

this methodology is similar to that described in Eklund and Ingvar's (2016) fMRI-based account on filled vs. unfilled hesitation pauses.


Isolated disfluencies with SPs 158 Total 404 Table 1.

it was demonstrated that annotated multichannel corpora like RUPEX can be an important resource for experimental research in interdisciplinary fields.


Since the focus of this work is to introduce a new dataset for tasks involving entity salience, we run simple algorithms to showcase the use of our dataset.

News articles in Wikinews are written by volunteers, who can write or edit a page by expanding it, correcting facts and so on.


Given an article, there might be hundreds or thousands readers, while there can only be one or few writers.

we observe that basic statistics show major differences between news articles in different years.


If there was any disagreement in the annotation, all the annotators discussed and made the final decision.

these teachers used minimal edits to make the learners' sentences grammatically correct.


Many of the articles in the Lang-8 corpus are written as if the learner writes a diary.

(2018) manually performed grammatical error correction limiting error types and adding error tags in the Lang-8 corpus to study a grammatical error correction system on Japanese functional expressions.


The Lang-8 corpus has often only one corrected sentence per learner sentence, which is not enough for evaluation.

we created and released a highly reliable evaluation corpus for a grammatical error correction system of JSL learners' sentences.


We decided to not make corrections on the sentence but on the article level.

in this study, we manually corrected the learner sentences extracted from the Lang-8 corpus using consistent rules and created a highly reliable evaluation corpus for the correction of grammatical errors in Japanese.


(Kleedorfer et al., 2008) apply Nonnegative Matrix Factorization (NMF) to ca. 60k song texts and cluster them into 60 topics

researchers have resorted to distant supervision, obtaining gold labels from social tags from lastfm.


The Odinson index loading times are not averaged, since they were loaded only once per query.

we introduce Odinson, a novel rule-based information extraction (IE) framework, which efficiently indexes syntactic information to rapidly execute patterns over a large corpus.


Here we extend this expressivity, allowing for both surface and syntactic information in the same pattern, i.e., a single Odinson pattern can include a regular expression over both surface tokens and dependency syntax.

because of the + marker on the instrument line, the rule also matches an additional instrument argument by continuing through the optional conj dependency.


Other fields are helpful to index but not required, such as lemma, chunk, entity, and part-of-speech tags.

a state-of-the-art Odin grammar for this domain had to backoff to surface information for 38% of its patterns (Valenzuela-Escárcega et al., 2018).


In the example, two sentences match the query, but only one of them satisfies the metadata filter.

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


We describe the sub-tasks with the help of the following examples.

extracting relevant information manually about any event from this large amount of data is a nearimpossible task.


Their proposed method automatically learns which parts are relevant for a given classication.

a neural network can learn those features automatically.


If a company voluntarily hides a vital information for the market's wealth, they fall under the Financial Authority it depends on and might become the subject of a lawsuit for Statement Fraud.

since the company is making business in every continent and sometimes in politically unstable countries, the risk has to appear in its Annual Report.


Also, the repartition of those sectors vary between countries, due to countries market's structure such as France having more "Consumer Staples" 2 companies due to the important share of the agricultural sector.

figure 5. shows that there is no pattern except that this section rarely occur near the end of ARs.


More specifically, relation discovery was cast as a prediction of the most likely relation type between two concept entities.

in EEG reports, certain words tend to be more ambiguous than others, suggesting that computing the encodings of these words requires additional processing to correctly capture their meaning from the contexts in which they appear.


Finally, the clinical correlation section explains what the EEG findings mean in terms of clinical interpretation, (e.g. "findings indicative of underlying metabolic encephalopathy").

the TNE leverages Adaptive Computation Time (Graves, 2016) to dynamically allocate more computational resources for the encoding of some words compared to others in the same EEG report.


This relation schema is adapted from the schema reported in previous work (Maldonado et al., 2017b;Maldonado et al., 2018).

several clinically relevant relations between concepts have also been identified and annotated in the corpus.


Since some unit pairs only make sense within a larger context, we also displayed the full microtext for every pair.

we want to investigate whether the distribution of the semantic properties we annotated for the inserted sentences -commonsense relation types and semantic clause types -respectively differs depending on the internal structure of an argument.


Retrieved URIs are stored as part of the entity annotation.

in addition, each of these tools has a specific functionality, while the Lynx platform combines them all in a single ecosystem.


We are interested in a small fraction of the information belonging to these domains.

in the following we briefly review several of these systems, which usually focus on one very specific feature or functionality.


This is potentially because the explicit relations are a smaller set and easier for BERT to predict and this in turn helps Spatial BERT.

we also relax the accuracy metric by counting a prediction as correct if the gold relation is in the top-5 predicted relations.


Each sequence contains complete sentences.

they can be difficult to implement because of the amount of data and computational resources needed for pretraining.


The existing automatic evaluation methods for these are limited (Lin et al., 2011; Pitler et al., 2010; Ellouze et al., 2017), usually do not take into account the complex and subjective nature of the linguistic quality factors.

furthermore, this work does not include any special data cleaning or annotation aggregation method other than the calculating mean values over 24 different judgments for a single item.


For the particular case of query-based extractive forum summarization, the source document consists of two bases: forum posts and the corresponding user query.

further analysis needs to be performed in order to find out the optimal aggregation method along with the corresponding optimal repetition number, such that comparable results to the laboratory can be obtained in a reliable and cost-effective way.


NH 2.0 contained 5,589,323 English words and and 2,651,414 Inuktitut words.

The Nunavut Hansard Inuktitut–English Parallel Corpus 3.0 (NH 3.0) consists of 17,330,271 English words in 1,452,347 sentences, and 8,068,977 Inuktitut words in 1,450,094 sentences, yielding approximately 1.3 million aligned sentence pairs.5


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

the final 10,000 sentences with their length information are written to a file in the tab separated format (tsv).


To improve the accuracy of curation classifiers, we do the following.

this method works for very popular temples like 'tirumala Venkateswara temple'.


A scientific study of temples can reveal valuable insights into Indian culture and heritage.

the temple corpus consists of 4933 high accuracy facts about 573 temples.


The generation of the boards happens manually in one of the applications, either by adapting existing available free boards 11 or creating completely new boards, which demands a high amount of manual work.

(Small) language resources created for endangered and under-studied languages can be exploited for various applications for people with limitations, especially when utilizing further information and annotations in the resource instead of focusing on a large amount of data.


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.

Details of the evaluation datasets are shown in Table 2.


We did not consider Japanese and Russian because they do not use the Latin alphabet.

in the case of OOV source words, we apply the copy baseline.


A total of 100k articles were scrapped from the archive in the encoded format.

if two or more annotators marked a given sentence as unsure, then such sentences were removed from the dataset in order to avoid ambiguity.


Moreover, it can be observed that consistent performance improvements are seen for all the four three metrics of evaluation: Precision, Recall, F1-Score, and Accuracy.

the annotators were advised to strictly avoid making annotation decisions based on their own point of view (e.g -personal prejudices) when it comes to such sentences.


Last but not least, one should ensure that the set of learners consulted for a given question is heterogeneous enough in terms of proficiency and background to prevent the unlikely, yet possible, situation where an incorrect answer is excessively chosen by a too homogeneous set of learners with similar shortcomings.

we could also observe that we were not able to identify exercises for some of the LRs targeted by our members such as Multiword-expression (MwE) datasets or Morphological Rules (see next section).


Usually, users tend to express their intention with short and lack of context utterances so traditional rule-based or machine-learning methods are not good enough.

in our experiments we use the same fine-tuning strategy as the original paper, feeding the output [CLS] token representation to an output layer for classification.


We have already mentioned that we took FMTODes as starting point for projecting the training data into Basque.

the number of epochs to fine-tune was selected based on the results from monolingual experiments (table 3), which were optimized on the dev set, to the point were standard deviation of micro F1-score between the five runs was lower than 1.


As evoked in section 2, we have adopted the expert-sourced expand approach to building our wordnet resource, i.e. a subset of words, glosses, and examples from the English Princeton WordNet were translated and validated by Gaelic language experts.

our USGW has its fair share of verbs but is relatively poor in adjectives with respect to the PWN (that has 15% of adjectives), which should be addressed in future work.


Both versions were evaluated over our test dataset.

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.


Overall, we have found that almost all types are strongly skewed towards either idiomatic or literal usage, and that truly balanced types are rare.

since this latter category is a 'miscellaneous'-category that can contain anything unforeseen, it triggers the third tier.


PIEs are most frequent in news, prose fiction, conversations, and popular magazines (pop lore), i.e. texts whose main purpose is entertainment.

We were however encouraged by related work by Kato et al.  (2018), who use crowdsourcing for a similar annotation task (verbal multiword expressions, including some idioms), showing that annotators agree in approximately 67% of cases.


For 'other'-labels, we also collected annotators' explanations for selecting that label.

what we have done here in terms of analysis is limited to a relatively high-level view of idiom distributions and frequencies.


As such, we collect more annotations for these lowagreement cases, until agreement is over 70%, up to a maximum nine annotations.

we compromise a little bit on precision to get a higher number of expressions.


The consonant system mostly relies on a three-way distinction in plosives /b, t, k/ and nasals /m, n, N/.

single word utterances tend to delete the final burst and therefore end with a mere closure [p^, t^, k^].


In addition to the language corpora, catalogue resources were generated and made available online.

while the large-scale approach of the project allows for manifold cross-lingual, diachronic and interdisciplinary research on a very high level, the data situation that will be described in the following section puts new requirements to methods of data modelling and technology used within the project.


Thereby, major challenges arise from the need for cross-resource data analysis and a rather complex resource landscape on the territory of the Russian Federation.

in practice it seems to be common to only extract parts of information that is considered to be relevant and to leave the remaining information behind.


When A is not a modal verb, the construction was treated as separate words.

we leave these instances open to future researchers' interpretation.


Section titles are found inside a capital letter and two line breaks.

besides, we perform ten different experiments with each kind of filter by applying different random seeds, as we must make a valid statistical comparison to ensure that the results are not coincidental.


Our work is focused on English, Swedish and Bulgarian, since the development of solid resources for other languages requires language expertise.

most ex-isting resources are aligned on synset level with Princeton WordNet, but as we will show later, synset alignment is not the same as building a translation dictionary.


It started with the primary goal of supporting Wikipedia, and in particular it provides structured, linked data about anything that has a Wikipedia article (called a topic).

the ShEx files are all publicly available under a CC0 license (Creative Commons, 2009), and thus can be reused and modified as needed for the given use case.


We also plan to expand this effort to cover Arabic dialects following the effort by Bouamor et al.  (2018) on the MADAR project; and target non-native speakers of Arabic (Saddiki et al., 2015; Saddiki et al., 2018).

level II: Generally corresponding to Grades 2-3, this level begins to add an imaginative dimension to the sensory which is still dominant in this level.


The formula is constructed based on selected features that are associated with text's lexical content and are considered important to determine the text's readability.

We report on a detailed analysis of the major disagreements among our annotators.


This information is repeated for all the vocabulary modules, although there is no way to go between languages on the TUFS website.

translation sets that did not link directly are discussed further below.


Table 4 show quantity information from OFrLex per UPoS.

we filtered it using the dedicated ghost words base named Base des mots fantômes [du Godefroy]12 dedicated to identify these entries and to clean them.


Performance on Japanese (ja) beats the high-performing baseline because of a feature of the Japanese writing system: foreign words are written in katakana, while native words are written in hiragana or kanji.

mW lists the first use of machine as 1545, though it was not found in GNG until after 1700.


In principle, there exists a set of ISO 1 rules which can be applied when converting a proper noun from a source language into a different target language.

to (Merhav and Ash, 2018), we used any language in the langtag of Wikipedia next to the word.


The obtained values are used, later, for estimating the presence of Framing Bias by comparing, for each election and news outlet, whether the subjectivity of the news outlet's reports about two opposing parties/candidates are significantly different or not.

each comment is finally represented by a five-dimensional subjectivity vector, where each dimension corresponds to the amount of a specific type of subjectivity.


Here, we provide the resulting data in a format that is used in a series of shared tasks for Translation Inference Across Dictionaries (TIAD-2017, TIAD-2019, TIAD-2020) for which UPM Apertium served as training data.

if the e2 argument is an entry with another language code, we point to the URi of the corresponding expression, i.e., a lexical form.


At the moment, we provide a generic RDF conversion for each of the dictionary collections, we do not harmonize external vocabularies beyond the application of the OntoLex-Lemon vocabulary.

each of these XML files is then transformed with an XSLT script to an RDF/XML representation in OntoLex-Lemon, with the concept mapping as described above.


Through structured discussion papers based on the results of the questionnaires the workshop participants (20-30 participants in each workshop) identified the problem areas and proposals for the most effective actions.

in this paper, we describe the process behind the development of the languagerelated parts of the strategy: A Danish Language Technology Committee was constituted and a comprehensive series of workshops were organised in which users, suppliers, developers, and researchers gave their valuable input based on their experiences.


The recommendations of the committee were finalized in January 2019.

governmental initiatives for artificial intelligence and especially for LT have been rather scarce.


Over the coming years, AI is expected to transform not only every industry but society as a whole.

there is a general initiative to foster digital transformation in industry, administration, and research.


Hence, it helps to shed light on the historical background of the socio-cultural being of Southern France's Gascon speaking minority, on linguistic and cultural interactions between the Gascon, Occitan and French speaking populations in the area.

a screenshot of the homepage is shown in Fig.


Only the top part of the ontologydescribing individual aspects of query functionality at an abstract level -will be standardized.

with conformance statements for a new (or newly added) CQL, or with entries for new Frames and Use Cases.


Conformance against the CQLF Metamodel is stated by providing the full path ending in a Module, e.g. (1) single stream / complex / dependency

there can be no positive conformance statement x that belongs to Q and A , nor to any Use Case U Ã¢Å â€˜ A instantiating A .


We developed a software to scrape sunnah.com pages and extracted the information from every Hadith.

the Hadith segmenter pipeline is shown in Figure 3, where it applies the following steps: 1.


One of the research areas that caught interest in AI methods is the study of religious texts to enhance understanding and discover new embedded knowledge.

to the best of our knowledge, no parallel corpus of Hadith is freely available to the research community.


The Hadith below is an example with parallel Isnad where the first chain of narrators is followed by the prophet's name, which is followed by another chain of narrators that ends with the prophet's name as well.

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


KPIs are not only considered useful for measuring progress, but also for collecting feedback on the strategy of a research infrastructure.

for new data sets, guidelines, best practices and awareness raising activities are planned that can help prevent avoidable interoperability gaps.


Out of these, one KPI is related to the use and promotion of standards and mappings that are essential for interoperability.

the ESFRI Monitoring group could not depart from actual objectives that were shared across infrastructures (there are some, but not many), but rather had to go for slightly more general objectives that could be agreed upon by all or many infrastructures, such as the aim to enable and support scientific excellence.


As of this writing, Related Works have been cataloged for roughly eighty percent of LDC's holdings and the data indicates that each corpus has an average of about 2.7 relations.

in order to support references to related works outside of the LDC Catalog, it was also necessary to store names and URLs for these exogenous resources.


In line with the project objectives, the scope for selecting institutions was confined in two ways

then we present the first step in how this objective was approached within a local language infrastructure project, namely by means of a structured documentation of the local language actors landscape in South tyrol.


Then we present the first step in how this objective was approached within a local language infrastructure project, namely by means of a structured documentation of the local language actors landscape in South Tyrol.

besides these principal players also a large number of smaller language actors could contribute to and benefit from language infrastructures.


We will be using only the tokenizer portions from these models as comparisons.

this is analogous to how subword tokenization methods have brought to the field guarantees of lossless encoding and decoding, which was not possible with conventional lossy encoding methods such as lemmatization, stemming, and other normalization methods.


Korean is an outlier in the CJK family, which linguistically has a shared vocabulary in terms of roots, but uses an entirely different character representation.

this also allows some level of sharing, which reduces the final budget needed in the vocabulary.


In modern electronic text corpora, cuneiform text is represented in two distict latinizations: (1) sign-to-sign level graphemic transliteration and (2) phonemic transcription based on an approximation of the Akkadian language and its reconstructed sound system (Kouwenberg, 2011).

it would be useful to minimize ambiguity by first using a large dictionary lookup (e.g. consisting of the whole corpus of a given dialect in Oracc), and then trying to predict the correct phonological rendering only if the transcription is clearly ambiguous


We hope out resource will foster NLP research in the financial domain where datasets are currently very scarce.

we observe a drop of 17% and 8% at the 3-and 4-gram levels respectively.


We used the BLEU score for evaluating our system performance.

we addressed this issue with BPE to make this whole process more efficient and reliable.


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

In self-attention architecture (Vaswani et al., 2017) at every time step of an RNN, a weighted average of all the previous states will be used as an extra input to the function that computes the next state.


After working on all these minor, but effective preprocessing we got our final dataset.

manual translation is a very tedious, costly, and time-taking process.


The noisier the training data becomes, the more the translation quality of NMT systems deteriorates.

the NMt model trained with only the content-equivalent corpus achieved a BLEU score of 20.93, which was the best score for Japanese→English news translation without using a domain-adaptation technique.


The wrong word (adequado) is shown in bold accompanied by the indication of the error subcategory (syn numberConc).

we believe that the worst performance of the NMT system was mainly due to the small size of the training corpus, which contains only about 162k sentences.


This approach has the advantage of being able to be applied to, possibly, all language pairs and corpora types.

the PBSMt was trained using Moses toolkit as detailed in (Martins and Caseli, 2015).


Subcategories of lexical errors are: extra word, absent word, not translated word and incorrectly translated word.

of this NMT approach's limitation, the most frequent error subcategories in the NMT system's output are all of the lexical error category: absent word (24.17%), incorrectly translated word (16.41%), not translated word (15.19%) and extra word (10.98%).


Numbers of BPE merge operations considered are 10k, 32k and 64k.

for under-resourced language pairs, the performance of MT systems can still be disappointing, as pointed out for instance by Koehn et al.


Figure 1(b) shows an example of a test for an article.

for preprocessing, the English sentences are tokenized and lowercased by the scripts in Moses toolkit (Koehn et al., 2007).


We first looked for examples in bilingual corpora.

As was also shown in Kimura et al. (2019), Japanese zero pronouns can basically be effectively handled by context-aware neural machine translation.


The translation output abandons the translation of the subordinate clause, which is a typical behavior of neural machine translation.

since we found that this approach is inefficient, we chose a different approach using linguistically annotated corpora.


The current NMT systems introduce the subword tokenization, which transfers rare words to the sequence of its constituent characters (Sennrich et al., 2016).

JPX has also enriched its childcare and caregiving leave systems to create an environment that allows employees to balance work and family commitments


As is well known, one bottleneck to MT is the scarcity of quality data, which means primarily parallel texts, but recently monolingual data has been usefully employed through the technique of back translation (Sennrich et al., 2015).

for this task we will use the systems and components from Wikifier (Brank et al., 2017), XLing (Rupnik et al., 2016) and EventRegistry (Leban et al., 2014) all dealing with statistical and semantic cross-lingual annotations and alignments.


Besides the aligned abstracts and sentences, we included the family id for each abstract, such that researchers can use that information for other text mining purposes.


Version 2 of the COPPA corpus contains almost 13 million parallel sentences in 8 language pairs.

amazon EC2 r5dn.8xlarge instances, featuring 32 cores and 256GiB of RaM memory were used for this alignment step.


Thus, the gains mainly come from better translation consistency contributed by document context.

this task aims to consider both the current sentence and its large context in a unified model to improve translation performances, especially in terms of discourse properties.


Taking news domain for example, one entity word usually needs to keep consistent translation across the whole document in newswire.

the collected corpora contain 7 language pairs (e.g.


Concatenative morphology is centered on stem and affix (prefixes, suffixes, circumfixes) morphemes, which are generally concatenated in a sequence to produce a surface form.

an example is the hamza ligature (combination of lam and hamza characters e.g lI, lO)) due to neighboring characters.


This result reinforces the observation we made in Table 2 that LIT methods are attractive for obtaining good performance with smaller vocabulary sizes.

weighted + PC removal: After creating word embeddings using (1), we substract the first Principal Component (PC) as suggested by Arora et al. (2017) to remove information that is common to all words, thereby emphasising the relative semantic differences among words.


For example, given the string "Hello world", where " " denotes the space character, a possible sequence of subtokens could be H/el/l/o/ /world.

as discussed later in section 5., training time of BPE is significantly longer compared to that of LM, which prevented us from creating 1M model for BPE.


To address this question,iIn a preliminary study, we mixed all corpora in Table 1 to create a single multilingual corpus and trained LM and BPE on it.

For example, text compression methods such as byte pair encoding (BPE) (Gage, 1994; Sennrich et al., 2016) and language modelling (LM) methods (Kudo, 2018) automatically select frequent subwords as tokens, and segment a given text such that some loss function (e.g. negative likelihood or code length) is minimised.


In de and fa where morphological agglutination and partial usage of fusional features are common (e.g. in the case system), we see that LIT methods such as BPE and LM outperform LST.

as seen from our previous example, unlike LST, LIT often produces nonsensical subwords, which are not valid morphological units (Zhu et al., 2019).


Exceedingly finer tokenisation is likely to return many irrelevant results with incorrect or partial matches, whereas not tokenising larger phrases will return zero results.

applying more sophisticated supervised composition methods such as a recurrent neural network might help to create word embeddings from subtoken embeddings under such situations.


We find that in very low settings, morphological analyzers help boost the performance of the full morphological disambiguation task.

we observe three regions of different performance behaviors.


We use the POS tagset introduced in (Habash et al., 2013) which consists of 36 tags.

non-lexical features are bounded in the language and therefore can be captured easily.


Since spelling modifications, especially insertions and deletions, will lead to a more complex full morphological evaluation process, we leave this effort to future work.

for GLF, we use the approach of paradigm completion as demonstrated by Eskander et al.


Morphological inflection is especially important and challenging for low-resource languages, where no or little annotated data is present.

model is trained to optimize perplexity without any supervision, therefore our approach differs from language model pretraining in the sense of Peters et al.  (2018) and other related works.


This issue is detailed further in Section 6.. Turkish morphology has a complex yet mostly regular morphotactics with a fixed order of morphemes with a notable exception concerning the interaction of the 3rd Person Plural morpheme -lAr with its surrounding context (Ozenc¸ and Solak, 2019b).

the number of analyses they return differs.


The BiLSTM model is used to monitor the probability of an end-of-token symbol appearing after any character within a given token sequence.

we also evaluate a version of this model with two BiLSTM layers.


The results of the evaluation show that the BiLSTM models obtain a much higher precision and recall than the statistical method in Kvistur 1.0.

this method identifies cognates, such as "Abdominalangiographie" in German and "abdominal angiography" in English, finding the split that maximizes the similarity between the constituent parts of the compound and the words in the non-compound.


adding more layers to the BiLSTM model does not appear to have a significant impact on precision and recall.


The UniSent lexica and a complete list of 1242 unique languages 4 covered by UniSent along with their language family information are provided in the supplementary material.

(ii) Manual-Train-Lexicon: In order to obtain an upper bound for the UniSent performance, we compare the use of UniSent-Train-Lexicon against the use of words in the gold standard lexicon as sentiment seeds for the training in the target domain.


Looking more closely at the neighbors, the word sensual in the biblical context has been associated with negative sentiment of sins.

The creation of UniSent requires only a sentiment lexicon in one language (e.g. English) and a small, but massively parallel corpus in a specific domain.


We have computed type to token ratio (TTR), calculated as vocabulary size divided by text length, based on the training transcriptions.

are also among the morphologically complex languages.


We presented a set of experiments meant to illustrate the usefulness of this resource.

examples in WIKIBANK consists of semantically labelled sentences, where each sentence has partial-semantic annotation for the predicate and its semantic arguments (see example in Table 2).


For example, for ES trained with 1000 target examples, the best model can be found in Table 5, for this example it is the one trained using DE as extra source language.

the attention mechanism focuses on encoding the frame representation that the parser has created rather than encoding the tokens themselves.


In order to avoid having the interviewers as common speakers across all sets, their utterances have been placed in the train set.

all participants were asked to report the languages they currently speak, as well as the languages learnt before the age of 5.


There is still a huge need to further collect corpora for the other language pairs, as well as extend the corpora for the previously mentioned languages, in order to give foundation for multilingual NLP applications to spur in that direction.

the train set contains utterances from 4 female and 8 male interviewees, in addition to 1 female and 1 male interviewer.


For k-best case, the maximum value across k predictions is taken.

as only Roman-script languages were used, the number of possible common acronyms makes it possible to use string similarity distance for monolingual clustering.


As shown in the mentioned paper, this approach helps to achieve maximum coverage despite transliteration ambiguity.

the bilingual model from (Merhav and Ash, 2018) returns predictions ranked by probability in descending order, but doesn't return actual probability values.


To analyse the model errors, we have sampled 200 random name pairs (English-Korean and English-Russian) for which the most probable three predictions of the model did not contain ground truth, i.e.

in our dataset only few of the variations are present.


The Entity linking task has been traditionally achieved on documents such as newspaper articles, which is rich in textual content but generally lacks visual information that could be used in the EL task.

each entity in the knowledge base represents a twitter user characterized by its timeline (set of pair of text-image).


For mention and entity textual context representations, we used the unsupervised Sent2Vec (Pagliardini et al., 2018) sentence embedding model, and more precisely, a pretrained version on a large Twitter corpus6

we have drawn inspiration from this usage to elaborate a simple process for both candidate entity and ambiguous mention generations.


We report in Table 3 the accuracy performance of our classifier for different feature combinations.

social media posts like tweets provide poor and noisy textual contexts which make the Entity linking task harder, but is often associated with complementary visual information.


Figure 1 shows a screenshot of the COCo platform.

we decided to concatenate each title to the general title of the lecture.


These predictions were combined to create the ensemble representation.

to ensure the content being published in the platform complies with the policies and guidelines, Youtube has moderators working intensively to review videos flagged by users.


Articles such as those on deforestation and drainage infrastructure are flood-related as they identify main (potential) causes of flooding.

future work in multimedia analysis should not assume that only a single type of connection is possible between text and image.


However, multimedia research often makes highly rigid assumptions about the relationship among the media.

the difference is not a sharp as one might expect.


We implement two neural models that use all modalities, TVQA (Lei et al., 2018) and MovieQA (Tapaswi et al., 2016).

these datasets include rich annotations about human actions, objects, and scenes, but do not include questions and answers as in LifeQA.


The graph shows that many questions reference basic visual features, such as count (how many),  color (what color), and location (where) answers.

we use GloVe embeddings (Pennington et al., 2014) with size 300 pretrained on 6B tokens from wikipedia 2014 (Rajpurkar et al., 2016) and Gigaword5 (Parker et al., 2011).


This paper provides a systematic evaluation of vector-space reduction variants across kinds, exploring reductions based on part-of-speech next to and also in combination with Principal Components Analysis using Singular Value Decomposition, and word2vec embeddings.

next to identifying a clear winner (Word2Vec) we can induce from our results that using only the most frequent noun dimensions is a reasonable alternative.


In a graph-based ranking model the terms extracted from the natural language text with a co-occurrence value within an n-sized window are represented as an undirected graph with nodes as single terms and co-occurrence values as unweighted edges.

we conclude that the meaning shifts are effective in biasing the network.


But also none of the results for the top k for DIY and ACL reach the results for COOK.

the automatic recognition of terms represents an important basis for further Natural Language Processing (NLP) tasks, such as thesaurus creation, automatic translation, and, in general, for domain knowledge acquisition and comprehension.


BPE tokenization has become a de-facto standard way for processing sub-words in the era of BERT (Devlin et al., 2019) and BERT-like models.

this is a list of 82 309 split nominal compounds extracted from a German wordnet GermaNet (Henrich and Hinrichs, 2010).


For the L2 speakers, we also collected more detailed information about their language background, as presented in Table 4.

in this project we developed a set of tools and language resources that were considered crucial for this kind of multifaceted research.


The Federal Patent Court (BPatG) dataset contains the lowest number of annotated entities (10.41 %).

the dataset was originally annotated by the first author.


A case report can contain more than one case description.

Grouin et al. (2019) presented a corpus with medical entity annotations of clinical cases written in French, Ju et al. (2019) presented a corpus focusing on phenotypic information for chronic obstructive pulmonary disease while Smalheiser et al. (2019) presented a corpus focusing on identifying main finding sentences in case reports.


Additionally, span-based F1 score is used to serialize the best performing model.

as mentioned above, only case presentation sections, headings and abstracts are annotated.


The parser is fully defined in that unknown characters or rules will yield separate tokens.

when sampling pages, no visible differences to the content or format was significantly evident.


due to the discreteness of the generated text, the gradients of sentiment classification loss could not be directly propagated from the classifier to our VAE model.


In the Machine Learning community, similar problems are typically solved by using artificially generated data to augment or perhaps even replace an original dataset (Bachman, 2016) in e.g. image processing.

since GPT-2 has been pre-trained on 40GB of internet text data, it has already learnt to model the English language and therefore needs considerably less data to achieve reasonable results and effectively learn how to write a discharge summary.


We use the fine-tuning scripts provided by nshepperd.

interestingly, this benefit only manifests itself when classifying with the BioBERT model.


The remainder of this paper is organized as follows.

to this end, we propose two stages of representation learning.


As a future work, we plan to apply our method to other tasks such as named entity recognition using ProBase (Wu et al., 2012) which has a number of entity information as a form of semantic networks.

the proposed model achieves as much as 8.1% improvement on average in test accuracy compared to strong baselines in the CR dataset.


Common Crawl word vectors (FastText-officialcommon-crawl) were trained on Common Crawl and Wikipedia using CBOW with position-weights, with character n-grams of length 5, a window size 5 and 10 negatives (Grave et al., 2018).

table 1 shows the composition of the corpus.


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

in many cases the teams that developed the algorithms also release their models, which facilitates both reproducibility and their application in downstream tasks.


Being a clever sort, he started shouting loudly, "Let me through!

second, we propose an exploratory study investigating the links between smiling and humor.


In line with such studies, laughter has even been considered "the contextualization cue for humor par excellence" (Kotthoff, 2000), and its absence has been seen as a mark of failure of humor (Norrick, 1993).

first, the entire humorous sequences were annotated and classified either as canned joke (CJ) or conversational humor (CH).


From the audio file of each speaker, the Inter-Pausal Units (IPUs) were automatically extracted.

it was recorded following the American protocol, as closely as possible, especially concerning the tasks given to the participants (see section 2.2).


Calculate the overlap between the reference sentence, Refs, and the raw prediction, Pred, as well as between Refs and the modified prediction, Pmod.

the highest value in each column is indicated in Figure 2a and shows the relationship between each source token and its corresponding target token.


In fact, Koehn and Knowles (2017) list raising the quality of out-of-domain translations as the first of "Six Challenges for Neural Machine Translation."

the training stage in NMt prepares a translation model from aligned bitexts in a given domain.


Furthermore, our results, based on limited data, indicated that percentage improvement increases, on average, with the size of the corpus as measured with BLEU, though this may not be true in all cases.

as discussed earlier, injection of target terminology sometimes introduces target-language grammatical errors when incompatibility with the rest of the sentence arises.


In both Figures 1b and 2a, the highest value in the column under the source token "report," a value of 0.8274, triggers the target token selection.

(1) Your report is absolutely disgraceful.


Dialogue data, techniques, and evaluation Social bots are expected to entertain the user hence and are often evaluated by the number of turns they can make in conversations (Khatri et al., 2018).

starting from a user experience perspective, what would be the ideal world experience for the interrogator?


and both TF-IDF and BERT models pick up the right question in the KB: 'Where are you from?'.

we considered the similarity between a new question and every answer in the KB as an alternative model, but the results are so much weaker than question-similarity that it is not worth reporting them.


The object property spokenIn relates a dialect with an administrative or geographic location where this dialect can be found.

bgDialectsOnto, on the other hand, is designed to implement the methods from the IbGDialectsOntology interface and it handles only individuals.


We find that the formality of naming in these tweets correlates positively with their stance.

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.


To our knowledge, cross-and joint-domain training on the SemEval 2014 Task 4 datasets has not been analyzed so far.

as a special case of cross-domain training we expect performance to be optimal if D LM = T .


To summarize, we find that in order to correctly predict aspect-target based sentiment, the context sensitivity of the sentiment expression plays an important role in difficult examples.

we compare our method to two very strong baseline models: BERT-base and XLNet-base.


Moreover, we can notice throughout Figure 4-6 a significant gap in scores between ML and DL models.

from the previous confusion matrix, we have already observed that the DESCRIPTION did not perform well compared to other categories.


, it attributes a property to the restaurant of which it represents the atmosphere.

The room is a little gloomy and old-fashioned.


The results show that the proactive assistant behavior has been rated similarly positively as for the non-proactive one, where users initiated the dialog.

every subject interacted with both assistants and experienced both traffic conditions during the respective interaction phases.


In this work, we are dealing with SA at document level.


For embedding construction, we consider three types of corpora: polar, non polar and mixed.

the first third of each document therefore seems to contain relevant information to polarity classification.


It is also unclear how to use it in cases where shifting results in a neutral polarity expression (Taboada et al., 2011).

wordNet provides a variety of semantic relations between words, such as hypernymy, antonymy, entailment and derivational relatedness.


Table 1 shows some examples of nearduplicate tweets found in the previous version of the corpus.

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


We define two separate dimensions to conceptualize what we consider humorous.

in 2018 the top system used an evolutionary algorithm for training the system (Ortiz-Bejar et al., 2018), while in 2019 the top system performed fine-tuning over a multilingual BERT language model (ismailov, 2019).


If the user chooses the option "no", it will be recorded as a negative vote for that tweet (not humor) and no further questions will be asked.

this could indicate that the process of presenting test tweets to all users helps ruling out some low-quality annotations.


Current semantic annotation addresses lexical (ontologyderived) tags rather than functional semantic annotation (semantic roles), albeit the latter is being prepared by including semantic frame tags for verbs.

the last linguistic topic we will present here is the use of complex tense, mode and aspect.


The treebank contains linguistic annotation at four primary levels: lemma, part-of-speech (POS) and inflection, syntactic function ("edge labels") and dependency-head id's (attachment links).

'forta' (strong) is tagged <jpower> if combined with human or civitas nouns, but <jdegree> if combined with perception nouns ('bruo' -noise or 'lumo' -light).


Because attachment errors were counted separately, attachment direction arrows at the clause level were ignored when evaluating function tags (i.e. @<SUBJ and @SUBJ> were both counted as just @SUBJ, subject)

sequential attachments of second and later conjuncts to the first conjunct can easily, and automatically, be raised to parallel attachment, if corpus users wish to use the latter format.


Accuracy is measured based on a detailed error analysis of the grammatical sentences which were incorrectly annotated by the parser.

the adjective "red" in a sentence like "the car is red") functions as the sentential head and selects for a subject.


Section 2. highlights some key issues in Wolof and describes how these are addressed in the grammar.

figure 1: C-and f-structure of sentence (1) expressed in the perfective aspect through the combination of the na morpheme and the lexical verb.


Bhat et al. (2017a) presented the improvements of dependency parsing for HUTB by using syntactically rich features.

the original PS treebank caters to flexible word order of Urdu and this design feature makes is naturally compatible with a dependency structure.


In many cases this does not change the information available in the treebank.

the most widely noted difference among annotation models for syntax is probably the divergence between constituency models that conceive of the sentence structure as consisting of hierarchically organized phrases or constituents, and dependency models that represent the sentence structure as a graph where the tokens (words) are the edges.


The hyper parameters were tuned on English UD, by exploring 100 configurations.

In the pool of exciting methods that simplify the parsing task, our approach is closer in spirit to the approach of Zhang et al. (2017) for dependency parsing, and Marcheggiani et al. (2017) for semantic role labeling.


This leads us to say that the degree of interactivity of the spoken dialogues has little or no influence on the POS distribution.

false starts are explicitly annotated in the ODIL Syntax treebank to offer the possibility to avoid those noisy structures when using machine learning techniques.


There have been various attempts at universal ASR: "designing a universal phone recognizer which can decode a new target language with neither adaptation nor retraining" (Siniscalchi et al., 2008).

this approach consists of a shared multilingual encoder and language-specific projection layer.


Most mappings were initially encoded by non-experts with a few hours of training, but all were subsequently checked by the first author, a professional linguist with graduate training in phonetics and phonology.

the Allosaurus model described on the right side of Figure 3 can overcome both issues of those standard models by taking advantage of AlloVera.


Sentences are phonetically rich (consist of the entire Arabic phonemic inventory) and balanced (having the same appearance in the language).

the annotation work for each speaker has been saved in Praat label files of the type 'textGrid'.


It is considered to be the modern version of Classical Arabic (Al-Sobh et al., 2015) and is the language of formal speech in Arab countries, such as is used in governmental speeches, the education system and on the news.

based on the protocol requirements, a dataset can be suitable for FVC research which fulfills three criteria: 1) non-contemporaneity of recording sessions for each speaker, 2) using different speaking styles for the recordings of each speaker, and 3) usability for research and casework involving recording and transmission-channel mismatch.


We demonstrate the usefulness of the proposed framework by applying it to precisely describe and compare six contemporary MRC datasets.

we are interested in different types of the expected answer.


MRC is a generic task format that can be used to probe for various natural language understanding capabilities (Gardner et al., 2019).

we aim to establish which linguistic phenomena are probed by gold standards and to which degree.


We therefore calculate agreement by calculating the Euclidean distance instead of the majority vote and define agreement when two outputs have distances less than the threshold.

we annotate (1) explanation on why the most plausible answer span cannot be the answer and (2) which part of the question causes unanswerability.


For more details on this model, please refer to (Hu et al., 2019).

another distinction of our dataset lies in scale: (1) SQuAD2-CR contains approximately 10K humanlabeled annotations about cause in total, and these are propagated to all unanswerable questions on SQuAD 2.0 by semi-supervised learning.


The two inputs, s and q represent the segment text and a question.

this localization task has been out of scope for previous studies.


Combining facts to perform inference is an inherently noisy process that often drifts off-context to unrelated facts, a phenomenon referred to as semantic drift (Fried et al., 2015).

that do not have any supporting facts, and thus lack lexical-overlap edges), and for the annotator to then include those additional supporting facts to solidify the explanation.


"Passing through" is similar to "entering".

A number of recent datasets have been made available to support multi-hop inference models


We have created this dataset from the various computer science related journals of Elsevier (like ARTINT, COMNET etc).

it could be a benchmark dataset for span-of-words-based MRC systems in the domain of scholarly articles.


Little work has been done, however, in developing EHR QA models that learn to answer questions directly from pairs of questions and their answers in unstructured notes.

this dataset can be considered a good representative example of an open-domain QA task.


As these tests are not independent, they cannot be summarized by using, for example, a binomial test.

how do we decide whether the difference observed between two conditions is large enough to be considered reproducible?


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

yet, it remains unknown how many of these edits were used to clarify information, since Daxenberger and Gurevych (2012) did not expose the underlying reasons to edit texts, apart from obvious ones such as spelling or grammar edits.


we describe two datasets providing disambiguations in the form of Wikipedia pages.

to gain insights on the features of each sense-annotated corpora, we provide a small analysis on the entropy and ambiguity levels.


Surprisingly, the performance drops significantly on every dataset as the weight of the original word vectors is decreased.

(2) MSD-1030 has a more balanced score distribution and higher annotation consistency, compared to the other datasets.


Figure 3 illustrates the box-and-whisker plots that represent the distribution of the scores' variance and range for the pairs in MSD-1030 and three other datasets.

each question in this dataset asks whether a certain word has the same meaning in two given contexts, which is a binary classification task.


In this paper, we raise six concerns about existing word embedding benchmarks.

we will show in Section 5 that our data construction process does lead to better consistency and a more balanced score distribution.


A semantic attribute of a concept, such as "an apple is red", explicitly dictates a semantic aspect of the concept.

the resulting vectors are combined by element-wise multiplication, and subsequently fed into the MLP that is responsible for the final classification.


Our aim is to provide the linguistic and NLP research communities with a gold standard sense-annotated corpus of French, using WordNet Unique Beginners as semantic tags, thus allowing for interoperability.

for instance, they use Vehicle, Building and Container as subclasses of the Artifact supersense (Martinez Alonso et al., 2016).


As mentioned earlier, no automatic pre-annotation was proposed for nouns and no wild card was allowed in case of difficulty.

All other annotation projects using UBs as tags we know of also had to make adjustments, but to a more limited extent.


They are those proposed as generalization in Table 1, column 2.

annotators could consult the description of the target noun's English equivalent on the WordNet website -using the "show lexical info" option -in order to guide their decision, while keeping in mind the differences between the two tagsets.


Closed and open form information extraction are important and well studied NLP tasks (Banko et al., 2007; Wu and Weld, 2010; Berant et al., 2011; Fader et al., 2014).

exploiting unstructured data sources to obtain structured user attributes is a challenging research direction.


Class names were given by choosing the most generic word member that suits the common meaning, as well as the visual.

this skill allows humans to express themselves in more complex ways, such as in communicating ideas, opinions and arguments.


Our 20 verb classes and visual representations cover only 500 abstract verbs, and the possibility of having more is not excluded.

the survey was completed by approximately 100 participants.


First, we notice that using more data improves results for LR.Tfidf, whereas LR.LIWC results remain relatively stable.

section 6 discusses the early detection of mental health issues, and section 7 presents additional remarks and discusses the next steps in the current project.


This is due to the fact that some joke posts are humorous short stories, while others are simple puns.

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


Let us see an example for this issue: @USER @USER It's probably better to have an next to my name than a pink pussy hat on my head #MAGA #MakeAmericaGreatAgain In this section, we provide an intrinsic evaluation of the corpus by conducting cross-validation experiments.

we obtained 0.788 in macro F 1 -score in sequence labeling setting by using BERT, and explored the role of different features, also related to affect, in a standard text classification setting, with the aim to shed a better light on the properties which allow to distinguish between abusive and not-abusive swearing.


We found 154 tweets having more than one swear word, with a range of occurrences from 2 to 6 swear words.

based on F 1 -score, the best performance is achieved by using all the features except Twitter features 15 with the LSVC model.


Our results confirm that our annotation is robust based on the sequence labeling performance.

we calculated the percentage of swear word use in each class and the percentage of each swear word used over both classes.


The inter annotator agreement is 0.708, based on Cohen's Kappa coefficient, which denotes a substantial agreement.


We applied monolingual learning, cross-lingual learning and bilingual learning, using several classification algorithms to approach this task.

combining the word and character n-grams features can enable the models to capture the writing styles more comprehensively, which leads to better results.


Besides, n-grams features are very easy to compute and the logistic regression runs faster than deep learning methods.

machine translation methods may be less appropriate for cross-lingual learning using stylometric features.


In the following sections, we present and discuss the guidelines and the agreement scores.

we converted the normalization annotations to a list of binary decisions (e.g., normalize the token or not).


Italian is one of the most popular languages on the Internet, estimated to be the 9th most popular by w3techs, and was the 13th most popular language on Twitter in 2018.

the "entification" is often a subjective process without clear boundaries, that is driven by the ideology, worship and psychological and linguistic disposition of the individuals.


Otherwise, we consider the tweet to be from an urban setting.

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


Broadcast recordings are generally recorded in clean conditions using professional equipment and expertise.

this indicates a good generalization of the model and suggests that the model is robust enough for operation in real-world applications.


Currently, probably English is the language with the largest amount of available training data for speech recognition.

the second baseline is a model that we train from scratch on the 25 hours of automatically aligned oral history data.


As a default step of the acoustic model training, we train an i-vector extractor on the English data in this stage.


The set is manually transcribed and segmented.

this observation can contribute to the ongoing research on speech recognition for under-resourced language.


Segmentation of periods applies 4 criteria: 1) pause lasting for at least 300 milliseconds; 2) difference in height between the mean value of fundamental frequency over all the signal before the pause and the last value of fundamental frequency before the pause; 3) difference in height between the last value of fundamental frequency before the pause and the first one after the pause; 4) absence of hesitation (« euh ») just before or after the pause.

the data is organized by sequences of utterances, so one utterance can contain several periods.


Analor (Avanzi, Lacheret-Dujour, Victorri, 2008) is a semi-automatic segmentation tool developed within this framework.

this pilot corpus contains transcriptions of preparing a meal, meetings, conferences, radio transmissions, interviews, etc.


The code, trained models, and a live demo will be made available publicly.

the largest publicly available resource available for training text-to-speech systems is the IndicttS (Baby et al., 2016) corpus that contains about 8 hours of speech data for 13 Indian languages.


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.

newspapers contain texts from a wide variety of domains ranging from politics to sports and entertainment.


RTS occurs in the environment where a Rising tone becomes a low tone when it is either followed by a High or Falling tone as clearly seen in Figure 1.

a Deep Neural Network (DNN) (Chen et al., 2014) with 3 hidden layers is trained for Mizo tone recognition, using Keras toolkit (Chollet and others, 2015).


Since speech recognition technology is essential for these robots to function effectively, improving the accuracy of recognition of elderly speech has become an urgent issue since conventional speech recognition technology has not demonstrated sufficient accuracy when processing elderly speech.

although the number of speakers was insufficient, a mild positive correlation was found between age and WER (correlation coefficient: 0.59).


There are 16 dialects in Japan, and these dialects have been found to effect the accuracy of speech recognition (Kudo, 1996).

there was also a wider variety of speaking styles in our corpus than in the JNAS and S-JNAS corpora, so we thought that the spontaneous speaking style contained in the CSJ corpus might be more similar to utterances of our speakers.


The average age of S-JNAS participants is 67.6 years old, but Japan's life expectancy has risen to 84.2.

we had a sufficient number of participants in Nagasaki, but to match the conditions in Tokushima and Yamagata we also recorded the Nagasaki test and training data using the same speakers.


The list also includes common compounds, phrases, proper nouns, foreign words and new words listed in CMUdict.

the pronunciation of some IE words cannot be correctly transcribed.


Such an analysis is outside the scope of this project.

manually segmented transcriptions are available in both NIST SCTK's time-marked transcription format (Fiscus, 2008) and Praat's TextGrid format (Boersma and Weenink, 2019).


The wizard would, assuming the issue was not critical.

roughly five thousand word segments across 92 word types from the experiment (excluding contracted determiners, foreign words, non-words, and words with less than 30 instances) were labelled in a binary fashion as more or less native.


Studies show though that if the whole process is automated, the correlation to human judgments drops considerably, therefore keeping the need for high-effort manual annotations (Peyrard and Eckle-Kohler, 2017).

the next steps involve the verification of our results on other languages, which are for example available in the MultiLing data.


As there were reportedly problems with the ROUGE evaluation in MultiLing, we aim to further investigate this outcome with variations of the PyrEval setup.

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.


Furthermore, improvements measured with one metric do not necessarily lead to improvements when using others.

another problem is that the credibility of ROUGE was demonstrated for the systems which operated in the lowscoring range.


This strengthens dependencies among neighboring elements and makes the model distance-aware when it searches for lowlevel patterns in a sequence.

a summarization model trained via Reinforcement Learning with this metric as reward achieved higher scores in both human and ROUGEbased evaluation.


They do not require time-intensive training and can be computed within a few seconds.

the Fandom wikis, for example, use a category system, as most wikis do.


In addition to the articles, this covers metadata on media files, discussion pages, category overviews, special pages and other sites not relevant to our task.

the question how the quality of the summary really correlates to the score of the extraction remains.


We work with a corpus of final reports of publicly funded research projects provided by the Leibniz Information Centre for Science and Technology (TIB 3 ).

this has been remedied by more recent efforts, such as the altmetrics movement, that consider the impact of research beyond academia, for example, by analyzing mentions of research in traditional and social media, or tracking the sharing and reuse of resources and data (Piwowar, 2013; taylor, 2013).


We refer to these empirically grounded categories as "anticipated" impact since these indicator phrases were stated in reports, which may precede the transfer of science to society.

the intellectual, factual and material access of the public to research can be a challenging task due to a lack of language consistency across academic domains, domain-specific terminology, limited open access resources, and publishers' paywalls.


The fruit pictures correspond to the images used in the cover story; the robot and confederate pictures illustrates episodes of live conversations (Image taken from (Rauchbauer et al., 2019)).

a cover story framing the study as a neuromarketing experiment with the goal to extract the message behind a new advertisement campaign provided the topic of conversation.


Some of the human-elicited hypotheses contain patterns that spuriously correlate to some specific labels.

for the hard subsets, on the other side, the indications of the artificial patterns should be all different from gold labels.


Instead, they were only empirically false or unlikely to be true presumably based on the MTurk workers' real-world experiences.

this is the largest fact checking dataset of real claims and evidence documents to date; it will allow the development of fact checking systems that can effectively process claims that occur in the real world.


Such aspects of this example, however, is not recognized by word pairs alone.

the average length of the context in terms of number of tokens was about 81 (spanning multiple sentences).


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).

(2) We analyze and examine the effect of such delexicalization techniques on several state of the art methods in NLI and confirm that these methods can still achieve comparative performance in domain.


Boonkwan and Supnithi (2017) suggested that word segmentation should be trained in combination with POS tagging.

negative log-likelihood loss on the development set and Adam optimizer (Kingma and Ba, 2015) are used to optimize the model parameters.


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.

accordingly, the language model yields high performance boost for both novel and encyclopedia in most settings, while the improvement for news is often modest.


The authors also suggested a few fine-tuning methods to adapt the pretrained LM to downstream tasks including discriminative fine-tuning, slanted triangular learning rates, and gradual unfreezing.

in novel-news combination with similar initial F1 Score, the pretrained LM can only bring 0.85% improvement in F1 Score.


For uttering these responses in appropriate timings, it is not necessary to deeply understand the content of narrative.

furthermore, the degree of empathy shown by "response 6" is also thought to be higher than that by "response 5."


As mentioned above, this data has five listeners' responses to the same narrative speech.

the start time of response was mapped onto the nearest position of the following one in the narrative speech, considering that responses tend to be uttered right after a linguistic or phonetic boundary, namely: 1. clause boundary (linguistic) 2. pause longer than 200 milliseconds (phonetic)


The component Grammar allows the user to specify a finite grammar with two extensions.

as the performance scores of all these systems are close to maximum values, it is impossible to use traditional test set for comparison.


The Estonian Reference Corpus (ERC) was missing information about paragraph boundaries, which is crucial for sentence segmentation.

if such correspondence is relevant, a single layer with a joint attribute set must be created.


Arity of Verbal Predicates – Distribution of the arity of verbal nodes, where the arity is the number of dependency links with that node as head.

Maximal Non-Verbal Phrase – Mean and standard deviation of the dimension of the maximal nominal phrases in the constituent trees of the inputted text, where the dimension of a node is the number of terminal nodes beneath that node.


Maximal Non-Verbal Phrase – Mean and standard deviation of the dimension of the maximal nominal phrases in the constituent trees of the inputted text, where the dimension of a node is the number of terminal nodes beneath that node.

in the absence of an objective difficulty measure, not only do subjective judgments lead to potential discrepancies between different individual evaluations made by teachers and/or test developers (Francois et al., 2014), but it also makes it impossible to gain insight into a large number of quantitative features that have an impact on the difficulty of a text, and on the cognitive load involved.


As for linguistic annotations, a comparable standard does not exist, but different community standards co-exist and/or compete with each other, see Ide et al. (2017) for annotations in general and Cimiano et al. (2020, p.61-122 and 197-212) for RDF-based data models in particular.


For RDF conversion of generic data into RDF, two prominent examples are the CSV2RDF and R2RML (Das et al., 2012) suites which focus on tabular formats resp.

even after all resulting subgraphs have been processed and merged again, a significant amount of triples will be lost.


Due to software issues we ended up with 186 WOz dialogues where we had both WOz participants' ratings and MTurkers' ratings.

we also generated simulated dialogues between dialogue policies and simulated users and again asked MTurkers to rate them on the same aspects.


Children with ASD prefer more limited social interaction compared to children without ASD, hence measurement of eye gaze as a screening tool may be an important contribution in this area (Vargas-Cuentas et al., 2017).

another study presented an analysis of gaze aversion patterns distinguishing between positive and negative schizophrenia (Vail et al., 2017).


In the current study, we combine these two functions where reference is part of the interaction.

they did not address the integration of eye movements with speech.


Due to the inclusion of the "biographies" section, the dynastic histories in the corpus contain not just purely historical data but also information on many aspects of everyday life in China, including family stories, where data on gender relations could be found.

these terms could be called "star terms" because they are "connected" to most of the context terms, as well as between themselves (as shown later).


One way of studying would be to create word vectors, based on counting words in a close context of a term and then comparing these vectors and establishing similarity between gender terms.

this project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license.


It should be noted that it is not a comprehensive list of all such terms for every historical period.

gender terms in Modern Chinese cannot form the immediate basis of investigation into a corpus of Classical Chinese because of language change.


Note that all Sound Pattern of English (SPE)-style rules may be so encoded (Kaplan and Kay, 1994).

in this paper, we take an informationtheoretic view of phonotactic complexity, and advocate for a measure that permits straightforward cross-linguistic comparison: bits per phoneme.


First, we calculated the bitsper-phoneme for just the first three positions in the word, and then looked at the correlation between this word-onset bits per phoneme and the average (full) word length in phoneme segments.

no other tonal languages were included in the corpus, so all reported results are over 106 languages.


However, we are now in a position to exploit samples from p lex .

in this study, we demonstrate that surprisal and related measures are not subject to the practical obstacles raised by Miestamo, independently of whichever class of complexity they fall into.


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

better question understanding models should improve performance and generalization in tasks that require multi-step reasoning or that do not have access to substantial amounts of data.


Instead, COPYNET opted to count both groups, then subtract the number of cylinders from the number of objects.

a rule-based algorithm can map more than 93% of the annotations into a correct formal representation.


Controllable Text Generation is an important problem in NLP that has received significant attention in recent times.

rOUGE-based selection from the candidates favors paraphrases that have higher n-gram overlap with their respective source sentences, hence may capture source's meaning better.


Exemplar-as-Output: Baseline where the output is the syntactic exemplar.

table 3: Sample generations of the competitive models.


We restricted validators to individuals currently located in the US who self-reported as native speakers of English.

we see the shallowest slopes on phenomena with the worst performance: NPIS (0.0078) and ISLANDS (0.0036).


We study how intervening material affects the LMs' sensitivity to mismatches in agreement in BLiMP.

we consider frequent inclusion of a phenomenon in a syntax/semantics textbook as an informal proxy for what linguists consider to be core phenomena.


9 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).

GB_EIF_M: GB with Matern52 kernel and expected influence as acquisition function.


In order to obtain reasonable results, such grammars need to be lexicalized because otherwise the independence assumptions of the PCFG are violated because of semantic relations, for example, between a verb and its subject.

the start symbol will be anchored by the symbol that occurs most frequently as a whole sentence.


He then uses this model to translate sentences in the in-probe and out-probe sets.

bob can also add ''watermark sentences'' that have some distinguishable characteristics to influence the Alice model, making attack easier.


To guard against these attacks, Alice’s protection strategy may include random subsampling of training data or additional regularization terms.

for Alice, we found that classifiers were almost always predicting in, resulting the accuracy to be around 50%.


The additional classification task empowers our model to better capture the logicality in a story implicitly, namely, modeling the causal and temporal dependencies, inter-sentence coherence, and avoiding repetition.

and we generated stories using a topk sampling scheme (Fan et al., 2018) with k = 40 and a softmax temperature of 0.7 (Goodfellow et al., 2016) to balance the trade-off between diversity and fluency.


Some work has attempted to explicitly incorporate commonsense knowledge into language generation (Zhou et al., 2018;Guan et al., 2019;Yang et al., 2019b).

to leverage commonsense knowledge in pretrained language models, we resort to existing large-scale knowledge bases ConceptNet (Li et al., 2016b) and AtOMIC .