pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
Authors: Hou Pong Chan, Wang Chen, Irwin King
Date: 2020-06-02 00:00:00
Tasks: Decoder, General Classification, Sentiment Analysis, Sentiment Classification

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders
Authors: Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa, Mikel Artetxe
Date: 2020-05-29 00:00:00
Tasks: Decoder, Machine Translation, Natural Language Inference, Sentence, Translation

We propose a modular architecture of language-specific encoder-decoders that constitutes a multilingual machine translation system that can be incrementally extended to new languages without the need for retraining the existing system when adding new languages. Differently from previous works, we simultaneously train $N$ languages in all translation directions by alternately freezing encoder or decoder modules, which indirectly forces the system to train in a common intermediate representation for all languages. Experimental results from multilingual machine translation show that we can successfully train this modular architecture improving on the initial languages while falling slightly behind when adding new languages or doing zero-shot translation. Additional comparison of the quality of sentence representation in the task of natural language inference shows that the alternately freezing training is also beneficial in this direction.
DiscSense: Automated Semantic Analysis of Discourse Markers
Authors: Damien Sileo, Tim Van De Cruys, Camille Pradel, Philippe Muller
Date: 2020-06-02 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification, Sentence

Discourse markers ({\it by contrast}, {\it happily}, etc.) are words or phrases that are used to signal semantic and/or pragmatic relationships between clauses or sentences. Recent work has fruitfully explored the prediction of discourse markers between sentence pairs in order to learn accurate sentence representations, that are useful in various classification tasks. In this work, we take another perspective: using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets). These predictions allow us to study the link between discourse markers and the semantic relations annotated in classification datasets. Handcrafted mappings have been proposed between markers and discourse relations on a limited set of markers and a limited set of categories, but there exist hundreds of discourse markers expressing a wide variety of relations, and there is no consensus on the taxonomy of relations between competing discourse theories (which are largely built in a top-down fashion). By using an automatic rediction method over existing semantically annotated datasets, we provide a bottom-up characterization of discourse markers in English. The resulting dataset, named DiscSense, is publicly available.
Web Document Categorization Using Naive Bayes Classifier and Latent Semantic Analysis
Authors: Alireza Saleh Sedghpour, Mohammad Reza Saleh Sedghpour
Date: 2020-06-02 00:00:00
Tasks: Classification, Document Classification, feature selection, General Classification

A rapid growth of web documents due to heavy use of World Wide Web necessitates efficient techniques to efficiently classify the document on the web. It is thus produced High volumes of data per second with high diversity. Automatically classification of these growing amounts of web document is One of the biggest challenges facing us today. Probabilistic classification algorithms such as Naive Bayes have become commonly used for web document classification. This problem is mainly because of the irrelatively high classification accuracy on plenty application areas as well as their lack of support to handle high dimensional and sparse data which is the exclusive characteristics of textual data representation. also it is common to Lack of attention and support the semantic relation between words using traditional feature selection method When dealing with the big data and large-scale web documents. In order to solve the problem, we proposed a method for web document classification that uses LSA to increase similarity of documents under the same class and improve the classification precision. Using this approach, we designed a faster and much accurate classifier for Web Documents. Experimental results have shown that using the mentioned preprocessing can improve accuracy and speed of Naive Bayes availably, the precision and recall metrics have indicated the improvement.
NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature
Authors: Saif M. Mohammad
Date: 2020-05-31 00:00:00
Proceeding: ACL 2020 6

As part of the NLP Scholar project, we created a single unified dataset of NLP papers and their meta-information (including citation numbers), by extracting and aligning information from the ACL Anthology and Google Scholar. In this paper, we describe several interconnected interactive visualizations (dashboards) that present various aspects of the data. Clicking on an item within a visualization or entering query terms in the search boxes filters the data in all visualizations in the dashboard. This allows users to search for papers in the area of their interest, published within specific time periods, published by specified authors, etc. The interactive visualizations presented here, and the associated dataset of papers mapped to citations, have additional uses as well including understanding how the field is growing (both overall and across sub-areas), as well as quantifying the impact of different types of papers on subsequent publications.
Neural Speaker Diarization with Speaker-Wise Chain Rule
Authors: Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Yawen Xue, Jing Shi, Kenji Nagamatsu
Date: 2020-06-02 00:00:00
Tasks: speaker-diarization, Speaker Diarization

Speaker diarization is an essential step for processing multi-speaker audio. Although an end-to-end neural diarization (EEND) method achieved state-of-the-art performance, it is limited to a fixed number of speakers. In this paper, we solve this fixed number of speaker issue by a novel speaker-wise conditional inference method based on the probabilistic chain rule. In the proposed method, each speaker's speech activity is regarded as a single random variable, and is estimated sequentially conditioned on previously estimated other speakers' speech activities. Similar to other sequence-to-sequence models, the proposed method produces a variable number of speakers with a stop sequence condition. We evaluated the proposed method on multi-speaker audio recordings of a variable number of speakers. Experimental results show that the proposed method can correctly produce diarization results with a variable number of speakers and outperforms the state-of-the-art end-to-end speaker diarization methods in terms of diarization error rate.
A Robust Statistics Approach for Plane Detection in Unorganized Point Clouds
Authors: Abner Matheus Costa de Araújo; Manuel Menezes de Oliveira Neto
Date: 2020-04-01 00:00:00
Proceeding: Pattern Recognition 2020 4
Tasks: Self-Driving Cars

Plane detection is a key component for many applications, such as industrial reverse engineering and self-driving cars. However, existing plane-detection techniques are sensitive to noise and to user-defined parameters. We introduce a fast deterministic technique for plane detection in unorganized point clouds that is robust to noise and virtually independent of parameter tuning. It is based on a novel planarity test drawn from robust statistics and on a split and merge strategy. Its parameter values are automatically adjusted to fit the local distribution of samples in the input dataset, thus leading to good reconstruction of even small planar regions. We demonstrate the effectiveness of our solution on several real datasets, comparing its performance to state-of-art plane detection techniques, and showing that it achieves better accuracy, while still being one of the fastest.
Finger Vein Reprsentation using gan
Authors: Yang
Date: 2018-06-28 00:00:00
Proceeding: IEEE 2018 6

Finger Vein Reprsentation using gan
Transformer-Capsule Model for Intent Detection
Authors: Aleksander Obuchowski, Michał Lew
Date: 2020-02-07 00:00:00
Proceeding: Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 2
Tasks: Intent Detection, Intent Recognition, model

Intent  recognition  is  one  of  the  most  crucial  tasks  in  NLUsystems, which are nowadays especially important for design-ing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU net-work implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu , and WebApp.
Towards modelling SUMO attributes through WordNet adjectives: a Case Study on Qualities
Authors: Itziar Gonzalez-Dios, Javier Alvez, German Rigau
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Previous studies have shown that the knowledge about attributes and properties in the SUMO ontology and its mapping to WordNet adjectives lacks of an accurate and complete characterization. A proper characterization of this type of knowledge is required to perform formal commonsense reasoning based on the SUMO properties, for instance to distinguish one concept from another based on their properties. In this context, we propose a new semi-automatic approach to model the knowledge about properties and attributes in SUMO by exploiting the information encoded in WordNet adjectives and its mapping to SUMO. To that end, we considered clusters of semantically related groups of WordNet adjectival and nominal synsets. Based on these clusters, we propose a new semi-automatic model for SUMO attributes and their mapping to WordNet, which also includes polarity information. In this paper, as an exploratory approach, we focus on qualities.
Incorporating Localised Context in Wordnet for Indic Languages
Authors: Soumya Mohapatra, Shikhar Agnihotri, Apar Garg, Praveen Shah, Shampa Chakraverty
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Due to rapid urbanization and a homogenized medium of instruction imposed in educational institutions, we have lost much of the golden literary offerings of the diverse languages and dialects that India once possessed. There is an urgent need to mitigate the paucity of online linguistic resources for several Hindi dialects. Given the corpus of a dialect, our system integrates the vocabulary of the dialect to the synsets of IndoWordnet along with their corresponding meta-data. Furthermore, we propose a systematic method for generating exemplary sentences for each newly integrated dialect word. The vocabulary thus integrated follows the schema of the wordnet and generates exemplary sentences to illustrate the meaning and usage of the word. We illustrate our methodology with the integration of words in the Awadhi dialect to the Hindi IndoWordnet to achieve an enrichment of 11.68 {\%} to the existing Hindi synsets. The BLEU metric for evaluating the quality of sentences yielded a 75th percentile score of 0.6351.
English WordNet 2020: Improving and Extending a WordNet for English using an Open-Source Methodology
Authors: John Philip McCrae, Alex Rademaker, re, Ewa Rudnicka, Francis Bond
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

WordNet, while one of the most widely used resources for NLP, has not been updated for a long time, and as such a new project English WordNet has arisen to continue the development of the model under an open-source paradigm. In this paper, we detail the second release of this resource entitled {``}English WordNet 2020{''}. The work has focused firstly, on the introduction of new synsets and senses and developing guidelines for this and secondly, on the integration of contributions from other projects. We present the changes in this edition, which total over 15,000 changes over the previous release.
Exploring the Enrichment of Basque WordNet with a Sentiment Lexicon
Authors: Itziar Gonzalez-Dios, Jon Alkorta
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, POS, text-classification, Text Classification, Translation, Word Sense Disambiguation

Wordnets are lexical databases where the semantic relations of words and concepts are established. These resources are useful for manyNLP tasks, such as automatic text classification, word-sense disambiguation or machine translation. In comparison with other wordnets,the Basque version is smaller and some PoS are underrepresented or missing e.g. adjectives and adverbs. In this work, we explore anovel approach to enrich the Basque WordNet, focusing on the adjectives. We want to prove the use and and effectiveness of sentimentlexicons to enrich the resource without the need of starting from scratch. Using as complementary resources, one dictionary and thesentiment valences of the words, we check if the word of the lexicon matches with the meaning of the synset, and if it matches we addthe word as variant to the Basque WordNet. Following this methodology, we describe the most frequent adjectives with positive andnegative valence, the matches and the possible solutions for the non-matches.
Wordnet As a Backbone of Domain and Application Conceptualizations in Systems with Multimodal Data
Authors: Jacek Marciniak
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Information systems gathering big amounts of resources growing with time containing distinct modalities (text, audio, video, images, GIS) and aggregating content in various ways (modular e-learning modules, Web systems presenting cultural artefacts) require tools supporting content description. The subject of the description may be the topic and the characteristics of the content expressed by sets of attributes. To describe such resources one can just use some of existing indexing languages like thesauri, classification systems, domain and upper ontologies, terminologies or dictionaries. When appropriate language does not exist, it is necessary to build a new system, which will have to serve both experts who describe resources and non-experts who search through them. The solution presented in this paper used to resource description, allows experts to freely select words and expressions, which are organized in hierarchies of various nature, including that of domain and application character. This is based on the wordnet structure, which introduces a clear order for each of these groups due to its lexical nature. The paper presents two systems where such approach was applied: the E-archaeology.org e-learning content repository in which domain knowledge was integrated to describe content topics and the Hatch system gathering multimodal information about the archaeological site targeted at a wide audience, where application conceptualization was applied to describe the content by a set of attributes.
Inclusion of Lithological terms (rocks and minerals) in The Open Wordnet for English
Authors: Alex Tessarollo, Alex Rademaker, re
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We extend the Open WordNet for English (OWN-EN) with rock-related and other lithological terms using the authoritative source of GBA{'}s Thesaurus. Our aim is to improve WordNet to better function within Oil {\&} Gas domain, particularly geoscience texts. We use a three step approach: a proof of concept-level extension of WordNet, a major extension on which we evaluate the impact with positive results and a full extension encompassing all GBA{'}s lithological terms. We also build a mapping to GBA which also links to several other resources: WikiData, British Geological Survey, Inspire, GeoSciML and DBpedia.
Adding Pronunciation Information to Wordnets
Authors: Thierry Declerck, Lenka Bajcetic, Melanie Siegel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: LEMMA, TAG

We describe on-going work consisting in adding pronunciation information to wordnets, as such information can indicate specific senses of a word. Many wordnets associate with their senses only a lemma form and a part-of-speech tag. At the same time, we are aware that additional linguistic information can be useful for identifying a specific sense of a wordnet lemma when encountered in a corpus. While work already deals with the addition of grammatical number or grammatical gender information to wordnet lemmas,we are investigating the linking of wordnet lemmas to pronunciation information, adding thus a speech-related modality to wordnets
Back and Forth between Theory and Application: Shared Phonological Coding Between ASL Signbank and ASL-LEX
Authors: Amelia Becker, Donovan Catt, Julie A. Hochgesang
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The development of signed language lexical databases, digital organizations that describe different phonological features of and attempt to establish relationships between signs has resulted in a renewed interest in the phonological descriptions used to uniquely identify and organize the lexicons of respective sign languages (van der Kooij, 2002; Fenlon et al., 2016; Brentari et al., 2018). Throughout the mutually shared coding process involved in organizing two lexical databases, ASL Signbank (Hochgesang, Crasborn and Lillo-Martin, 2020) and ASL-LEX (Caselli et al., 2016), issues have arisen that require revisiting how phonological features and categories are to be applied and even decided upon, and which would adequately distinguish lexical contrast for respective sign languages. The paper concludes by exploring the inverse of the theory-to-database relationship. Examples are given of theoretical implications and research questions that arise from consequences of language resource building. These are presented as evidence that not only does theory impact organization of databases but that the process of database creation can also inform our theories.
Improving and Extending Continuous Sign Language Recognition: Taking Iconicity and Spatial Language into account
Authors: Valentin Belissen, Mich{\`e}le Gouiff{\`e}s, Annelies Braffort
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sign Language Recognition, Translation

In a lot of recent research, attention has been drawn to recognizing sequences of lexical signs in continuous Sign Language corpora, often artificial. However, as SLs are structured through the use of space and iconicity, focusing on lexicon only prevents the field of Continuous Sign Language Recognition (CSLR) from extending to Sign Language Understanding and Translation. In this article, we propose a new formulation of the CSLR problem and discuss the possibility of recognizing higher-level linguistic structures in SL videos, like classifier constructions. These structures show much more variability than lexical signs, and are fundamentally different than them in the sense that form and meaning can not be disentangled. Building on the recently published French Sign Language corpus Dicta-Sign-LSF-v2, we discuss the performance and relevance of a simple recurrent neural network trained to recognize illustrative structures.
Utterance-Unit Annotation for the JSL Dialogue Corpus: Toward a Multimodal Approach to Corpus Linguistics
Authors: Mayumi Bono, Rui Sakaida, Tomohiro Okada, Yusuke Miyao
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

This paper describes a method for annotating the Japanese Sign Language (JSL) dialogue corpus. We developed a way to identify interactional boundaries and define a {`}utterance unit{'} in sign language using various multimodal features accompanying signing. The utterance unit is an original concept for segmenting and annotating sign language dialogue referring to signer{'}s native sense from the perspectives of Conversation Analysis (CA) and Interaction Studies. First of all, we postulated that we should identify a fundamental concept of interaction-specific unit for understanding interactional mechanisms, such as turn-taking (Sacks et al. 1974), in sign-language social interactions. Obviously, it does should not relying on a spoken language writing system for storing signings in corpora and making translations. We believe that there are two kinds of possible applications for utterance units: one is to develop corpus linguistics research for both signed and spoken corpora; the other is to build an informatics system that includes, but is not limited to, a machine translation system for sign languages.
Measuring Lexical Similarity across Sign Languages in Global Signbank
Authors: Carl B{\"o}rstell, Onno Crasborn, Lori Whynot
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Lexicostatistics is the main method used in previous work measuring linguistic distances between sign languages. As a method, it disregards any possible structural/grammatical similarity, instead focusing exclusively on lexical items, but it is time consuming as it requires some comparable phonological coding (i.e. form description) as well as concept matching (i.e. meaning description) of signs across the sign languages to be compared. In this paper, we present a novel approach for measuring lexical similarity across any two sign languages using the Global Signbank platform, a lexical database of uniformly coded signs. The method involves a feature-by-feature comparison of all matched phonological features. This method can be used in two distinct ways: 1) automatically comparing the amount of lexical overlap between two sign languages (with a more detailed feature-description than previous lexicostatistical methods); 2) finding exact form-matches across languages that are either matched or mismatched in meaning (i.e. true or false friends). We show the feasability of this method by comparing three languages (datasets) in Global Signbank, and are currently expanding both the size of these three as well as the total number of datasets.