pwc-archive/papers-with-abstracts huggingface dataset:
Sign Language Recognition with Transformer Networks
Authors: Mathieu De Coster, Mieke Van Herreweghe, Joni Dambre
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Keypoint Estimation, Sign Language Recognition

Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7{\%} on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation.
Annotating a Fable in Italian Sign Language (LIS)
Authors: Serena Trolvi, Rodolfo Delmonte
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper introduces work carried out for the automatic generation of a written text in Italian starting from glosses of a fable in Italian Sign Language (LIS). The paper gives a brief overview of sign languages (SLs) and some peculiarities of SL fables such as the use of space, the strategy of Role Shift and classifiers. It also presents the annotation of the fable {``}The Tortoise and the Hare{''} - signed in LIS and made available by Alba Cooperativa Sociale -, which was annotated manually by first author for her master{'}s thesis. The annotation was the starting point of a generation process that allowed us to automatically generate a text in Italian starting from LIS glosses. LIS sentences have been transcribed with Italian words into tables on simultaneous layers, each of which contains specific linguistic or non-linguistic pieces of information. In addition, the present work discusses problems encountered in the annotation and generation process.
HamNoSyS2SiGML: Translating HamNoSys Into SiGML
Authors: Carolina Neves, Lu{\'\i}sa Coheur, Hugo Nicolau
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Sign Languages are visual languages and the main means of communication used by Deaf people. However, the majority of the information available online is presented through written form. Hence, it is not of easy access to the Deaf community. Avatars that can animate sign languages have gained an increase of interest in this area due to their flexibility in the process of generation and edition. Synthetic animation of conversational agents can be achieved through the use of notation systems. HamNoSys is one of these systems, which describes movements of the body through symbols. Its XML-compliant, SiGML, is a machine-readable input of HamNoSys able to animate avatars. Nevertheless, current tools have no freely available open source libraries that allow the conversion from HamNoSys to SiGML. Our goal is to develop a tool of open access, which can perform this conversion independently from other platforms. This system represents a crucial intermediate step in the bigger pipeline of animating signing avatars. Two cases studies are described in order to illustrate different applications of our tool.
Dicta-Sign-LSF-v2: Remake of a Continuous French Sign Language Dialogue Corpus and a First Baseline for Automatic Sign Language Processing
Authors: Valentin Belissen, Annelies Braffort, Mich{\`e}le Gouiff{\`e}s
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

While the research in automatic Sign Language Processing (SLP) is growing, it has been almost exclusively focused on recognizing lexical signs, whether isolated or within continuous SL production. However, Sign Languages include many other gestural units like iconic structures, which need to be recognized in order to go towards a true SL understanding. In this paper, we propose a newer version of the publicly available SL corpus Dicta-Sign, limited to its French Sign Language part. Involving 16 different signers, this dialogue corpus was produced with very few constraints on the style and content. It includes lexical and non-lexical annotations over 11 hours of video recording, with 35000 manual units. With the aim of stimulating research in SL understanding, we also provide a baseline for the recognition of lexical signs and non-lexical structures on this corpus. A very compact modeling of a signer is built and a Convolutional-Recurrent Neural Network is trained and tested on Dicta-Sign-LSF-v2, with state-of-the-art results, including the ability to detect iconicity in SL production.
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition
Authors: S Tornay, rine, Oya Aran, Mathew Magimai. -Doss
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Gesture Recognition, Model Selection

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM structure has still remained as a challenge, especially when the number of signs to be modeled is high. In this work, we present a continuous HMM framework for modeling and recognizing isolated signs, which inherently performs model selection to optimize the number of states for each sign separately during recognition. Our experiments on three different datasets, namely, German sign language DGS dataset, Turkish sign language HospiSign dataset and Chalearn14 dataset show that the proposed approach achieves better sign language or gesture recognition systems in comparison to the approach of selecting or presetting the number of HMM states based on k-means, and yields systems that perform competitive to the case where the number of states are determined based on the test set performance.
VROAV: Using Iconicity to Visually Represent Abstract Verbs
Authors: Simone Scicluna, Carlo Strapparava
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

For a long time, philosophers, linguists and scientists have been keen on finding an answer to the mind-bending question {``}what does abstract language look like?{''}, which has also sprung from the phenomenon of mental imagery and how this emerges in the mind. One way of approaching the matter of word representations is by exploring the common semantic elements that link words to each other. Visual languages like sign languages have been found to reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of identifying clusters of iconic meanings. With this insight, merged with an understanding of verb predicates achieved from VerbNet, this study presents a novel verb classification system based on visual shapes, using graphic animation to visually represent 20 classes of abstract verbs. Considerable agreement between participants who judged the graphic animations based on representativeness suggests a positive way forward for this proposal, which may be developed as a language learning aid in educational contexts or as a multimodal language comprehension tool for digital text.
MEDIAPI-SKEL - A 2D-Skeleton Video Database of French Sign Language With Aligned French Subtitles
Authors: Hannah Bull, Annelies Braffort, Mich{\`e}le Gouiff{\`e}s
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cross-Modal Retrieval, Retrieval, Semantic Segmentation, Video Semantic Segmentation

This paper presents MEDIAPI-SKEL, a 2D-skeleton database of French Sign Language videos aligned with French subtitles. The corpus contains 27 hours of video of body, face and hand keypoints, aligned to subtitles with a vocabulary size of 17k tokens. In contrast to existing sign language corpora such as videos produced under laboratory conditions or translations of TV programs into sign language, this database is constructed using original sign language content largely produced by deaf journalists at the media company M{\'e}dia-Pi. Moreover, the videos are accurately synchronized with French subtitles. We propose three challenges appropriate for this corpus that are related to processing units of signs in context: automatic alignment of text and video, semantic segmentation of sign language, and production of video-text embeddings for cross-modal retrieval. These challenges deviate from the classic task of identifying a limited number of lexical signs in a video stream.
Alignment Data base for a Sign Language Concordancer
Authors: Marion Kaczmarek, Michael Filhol
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Translation

This article deals with elaborating a data base of alignments of parallel Franch-LSF segments. This data base is meant to be searched using a concordancer which we are also designing. We wish to equip Sign Language translators with tools similar to those used in text-to-text translation. To do so, we need language resources to feed them. Already existing Sign Language corpora can be found, but do not match our needs: working around a Sign Language concordancer, the corpus must be a parallel one and provide various examples of vocabulary and grammatical construction. We started with a parallel corpus of 40 short news and 120 SL videos , which we aligned manually by segments of various length. We described the methodology we used, how we define our segments and alignments. The last part concerns how we hope to allow the data base to keep growing in a near future.
Evaluation of Manual and Non-manual Components for Sign Language Recognition
Authors: Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, S, Anara ygulova
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sign Language Recognition

The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign{'}s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2{\%} of accuracy for dataset with 20 signs and 77.9{\%} of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: https://krslproject.github.io/krsl20/
TheRuSLan: Database of Russian Sign Language
Authors: Ildar Kagirov, Denis Ivanko, Dmitry Ryumin, Alex Axyonov, er, Alexey Karpov
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Gesture Recognition, Sign Language Recognition

In this paper, a new Russian sign language multimedia database TheRuSLan is presented. The database includes lexical units (single words and phrases) from Russian sign language within one subject area, namely, {``}food products at the supermarket{''}, and was collected using MS Kinect 2.0 device including both FullHD video and the depth map modes, which provides new opportunities for the lexicographical description of the Russian sign language vocabulary and enhances research in the field of automatic gesture recognition. Russian sign language has an official status in Russia, and over 120,000 deaf people in Russia and its neighboring countries use it as their first language. Russian sign language has no writing system, is poorly described and belongs to the low-resource languages. The authors formulate the basic principles of annotation of sign words, based on the collected data, and reveal the content of the collected database. In the future, the database will be expanded and comprise more lexical units. The database is explicitly made for the task of creating an automatic system for Russian sign language recognition.
Issues and Perspectives from 10,000 Annotated Financial Social Media Data
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we investigate the annotation of financial social media data from several angles. We present Fin-SoMe, a dataset with 10,000 labeled financial tweets annotated by experts from both the front desk and the middle desk in a bank{'}s treasury. These annotated results reveal that (1) writer-labeled market sentiment may be a misleading label; (2) writer{'}s sentiment and market sentiment of an investor may be different; (3) most financial tweets provide unfounded analysis results; and (4) almost no investors write down the gain/loss results for their positions, which would otherwise greatly facilitate detailed evaluation of their performance. Based on these results, we address various open problems and suggest possible directions for future work on financial social media data. We also provide an experiment on the key snippet extraction task to compare the performance of using a general sentiment dictionary and using the domain-specific dictionary. The results echo our findings from the experts{'} annotations.
Searching Brazilian Twitter for Signs of Mental Health Issues
Authors: Wesley Santos, Am Funabashi, a, Iv Paraboni, r{\'e}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: text-classification, Text Classification

Depression and related mental health issues are often reflected in the language employed by the individuals who suffer from these conditions and, accordingly, research in Natural Language Processing (NLP) and related fields have developed an increasing number of studies devoted to their recognition in social media text. Some of these studies have also attempted to go beyond recognition by focusing on the early signs of these illnesses, and by analysing the users{'} publication history over time to potentially prevent further harm. The two kinds of study are of course overlapping, and often make use of supervised machine learning methods based on annotated corpora. However, as in many other fields, existing resources are largely devoted to English NLP, and there is little support for these studies in under resourced languages.To bridge this gap, in this paper we describe the initial steps towards building a novel resource of this kind - a corpus intended to support both the recognition of mental health issues and the temporal analysis of these illnesses - in the Brazilian Portuguese language, and initial results of a number of experiments in text classification addressing both tasks.
RedDust: a Large Reusable Dataset of Reddit User Traits
Authors: Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Attribute

Social media is a rich source of assertions about personal traits, such as {``}I am a doctor{''} or {``}my hobby is playing tennis{''}. Precisely identifying explicit assertions is difficult, though, because of the users{'} highly varied vocabulary and language expressions. Identifying personal traits from implicit assertions like I{'}ve been at work treating patients all day is even more challenging. This paper presents RedDust, a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age,and gender. We construct RedDust using a diverse set of high-precision patterns and demonstrate its use as a resource for developing learning models to deal with implicit assertions. RedDust consists of users{'} personal traits, which are (attribute, value) pairs, along with users{'} post ids, which may be used to retrieve the posts from a publicly available crawl or from the Reddit API. We discuss the construction of the resource and show interesting statistics and insights into the data. We also compare different classifiers, which can be learned from RedDust. To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale. We envision further use cases of RedDust for providing background knowledge about user traits, to enhance personalized search and recommendation as well as conversational agents.
An Annotated Social Media Corpus for German
Authors: Eckhard Bick
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, Sentence

This paper presents the German Twitter section of a large (2 billion word) bilingual Social Media corpus for Hate Speech research, discussing the compilation, pseudonymization and grammatical annotation of the corpus, as well as special linguistic features and peculiarities encountered in the data. Among other things, compounding, accidental and intentional orthographic variation, gendering and the use of emoticons/emojis are addressed in a genre-specific fashion. We present the different layers of linguistic annotation (morphosyntactic, dependencies and semantic types) and explain how a general parser (GerGram) can be made to work on Social Media data, pointing out necessary adaptations and extensions. In an evaluation run on a random cross-section of tweets, the modified parser achieved F-scores of 97{\%} for morphology (fine-grained POS) and 92{\%} for syntax (labeled attachment score). Predictably, performance was twice as good in tweets with standard orthography than in tweets with spelling/casing irregularities or lack of sentence separation, the effect being more marked for morphology than for syntax.
The rJokes Dataset: a Large Scale Humor Collection
Authors: Orion Weller, Kevin Seppi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cultural Vocal Bursts Intensity Prediction

Humor is a complicated language phenomenon that depends upon many factors, including topic, date, and recipient. Because of this variation, it can be hard to determine what exactly makes a joke humorous, leading to difficulties in joke identification and related tasks. Furthermore, current humor datasets are lacking in both joke variety and size, with almost all current datasets having less than 100k jokes. In order to alleviate this issue we compile a collection of over 550,000 jokes posted over an 11 year period on the Reddit r/Jokes subreddit (an online forum), providing a large scale humor dataset that can easily be used for a myriad of tasks. This dataset also provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. We explore this dataset through the years, examining basic statistics, most mentioned entities, and sentiment proportions. We also introduce this dataset as a task for future work, where models learn to predict the level of humor in a joke. On that task we provide strong state-of-the-art baseline models and show room for future improvement. We hope that this dataset will not only help those researching computational humor, but also help social scientists who seek to understand popular culture through humor.
EmpiriST Corpus 2.0: Adding Manual Normalization, Lemmatization and Semantic Tagging to a German Web and CMC Corpus
Authors: Thomas Proisl, Natalie Dykes, Philipp Heinrich, Besim Kabashi, Andreas Blombach, Stefan Evert
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Lemmatization

The EmpiriST corpus (Bei{\ss}wenger et al., 2016) is a manually tokenized and part-of-speech tagged corpus of approximately 23,000 tokens of German Web and CMC (computer-mediated communication) data. We extend the corpus with manually created annotation layers for word form normalization, lemmatization and lexical semantics. All annotations have been independently performed by multiple human annotators. We report inter-annotator agreements and results of baseline systems and state-of-the-art off-the-shelf tools.
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
Authors: Kai Nakamura, Sharon Levy, William Yang Wang
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, Cultural Vocal Bursts Intensity Prediction, Fake News Detection, General Classification

Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth of our dataset. We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters
Authors: S, Eric ers, Antal Van den Bosch
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We study the relation between the number of mentions of political parties in the last weeks before the elections and the election results.In this paper we focus on the Dutch elections of the parliament in 2012 and for the provinces (and the senate) in 2011 and 2015. With raw counts, without adaptations, we achieve a mean absolute error (MAE) of 2.71{\%} for 2011, 2.02{\%} for 2012 and 2.89{\%} for 2015. A set of over 17,000 tweets containing political party names were annotated by at least three annotators per tweet on ten features denoting communicative intent (including the presence of sarcasm, the message{'}s polarity, the presence of an explicit voting endorsement or explicit voting advice, etc.). The annotations were used to create oracle (gold-standard) filters. Tweets with or without a certain majority annotation are held out from the tweet counts, with the goal of attaining lower MAEs. With a grid search we tested all combinations of filters and their responding MAE to find the best filter ensemble. It appeared that the filters show markedly different behaviour for the three elections and only a small MAE improvement is possible when optimizing on all three elections. Larger improvements for one election are possible, but result in deterioration of the MAE for the other elections.
A Real-Time System for Credibility on Twitter
Authors: Adrian Iftene, Daniela Gifu, Andrei-Remus Miron, Mihai-Stefan Dudu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.