pwc-archive/papers-with-abstracts huggingface dataset:
MultiSeg: Parallel Data and Subword Information for Learning Bilingual Embeddings in Low Resource Scenarios
Date: 2020-05-01 00:00:00
Proceeding: WS 2020 5
Tasks: Cross-Lingual Word Embeddings, Translation, Word Embeddings, Word Similarity, Word Translation

Distributed word embeddings have become ubiquitous in natural language processing as they have been shown to improve performance in many semantic and syntactic tasks. Popular models for learning cross-lingual word embeddings do not consider the morphology of words. We propose an approach to learn bilingual embeddings using parallel data and subword information that is expressed in various forms, i.e. character n-grams, morphemes obtained by unsupervised morphological segmentation and byte pair encoding. We report results for three low resource morphologically rich languages (Swahili, Tagalog, and Somali) and a high resource language (German) in a simulated a low-resource scenario. Our results show that our method that leverages subword information outperforms the model without subword information, both in intrinsic and extrinsic evaluations of the learned embeddings. Specifically, analogy reasoning results show that using subwords helps capture syntactic characteristics. Semantically, word similarity results and intrinsically, word translation scores demonstrate superior performance over existing methods. Finally, qualitative analysis also shows better-quality cross-lingual embeddings particularly for morphological variants in both languages.
Scoring Interactional Aspects of Human-Machine Dialog for Language Learning and Assessment using Text Features
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

While there has been much work in the language learning and assessment literature on human and automated scoring of essays and short constructed responses, there is little to no work examining text features for scoring of dialog data, particularly interactional aspects thereof, to assess conversational proficiency over and above constructed response skills. Our work bridges this gap by investigating both human and automated approaches towards scoring human{--}machine text dialog in the context of a real-world language learning application. We collected conversational data of human learners interacting with a cloud-based standards-compliant dialog system, triple-scored these data along multiple dimensions of conversational proficiency, and then analyzed the performance trends. We further examined two different approaches to automated scoring of such data and show that these approaches are able to perform at or above par with human agreement for a majority of dimensions of the scoring rubric.
Determinant-based Fast Greedy Sensor Selection Algorithm
Authors: Yuji Saito, Taku Nonomura, Keigo Yamada, Kumi Nakai, Takayuki Nagata, Keisuke Asai, Yasuo Sasaki, Daisuke Tsubakino
Date: 2019-11-20 00:00:00

In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.
``How Bullying is this Message?'': A Psychometric Thermometer for Bullying
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Cyberbullying statistics are shocking, the number of affected young people is increasing dramatically with the affordability of mobile technology devices combined with a growing number of social networks. This paper proposes a framework to analyse Tweets with the goal to identify cyberharassment in social networks as an important step to protect people from cyberbullying. The proposed framework incorporates latent or hidden variables with supervised learning to determine potential bullying cases resembling short blogging type texts such as Tweets. It uses the LIWC2007 - tool that translates Tweet messages into 67 numeric values, representing 67 word categories. The output vectors are then used as features for four different classifiers implemented in Weka. Tests on all four classifiers delivered encouraging predictive capability of Tweet messages. Overall it was found that the use of numeric psychometric values outperformed the same algorithms using both filtered and unfiltered words as features. The best performing algorithms was Random Forest with an F1-value of 0.947 using psychometric features compared to a value of 0.847 for the same algorithm using words as features.
Impr\'ecision dans la production des voyelles : un potentiel marqueur infraclinique dans la maladie de Parkinson (Imprecision of vowel production: a potential subclinical marker in Parkinson's disease)
Date: 2020-06-01 00:00:00
Proceeding: JEPTALNRECITAL 2020 6

La maladie de Parkinson est une maladie neurod{\'e}g{\'e}n{\'e}rative qui affecte le syst{\`e}me neuro-moteur. Une grande vari{\'e}t{\'e} de troubles de la parole, g{\'e}n{\'e}ralement regroup{\'e}s sous les termes de dysarthrie hypokin{\'e}tique, peuvent apparaitre. Dans cette contribution, nous pr{\'e}sentons les r{\'e}sultats d{'}une {\'e}tude acoustique comparative de la production de voyelles par 63 locuteurs MP dysarthriques et non dysarthriques, avec 35 locuteurs sains. Notre objectif est d{'}{\'e}tudier la production de voyelles isol{\'e}es afin de r{\'e}pondre {\`a} la question : l{'}impr{\'e}cision dans la production des voyelles peut-elle {\^e}tre consid{\'e}r{\'e}e comme un marqueur infraclinique de la dysarthrie ?
N\'eonaute, Enrichissement s\'emantique pour la recherche d'information ()
Date: 2018-05-01 00:00:00
Proceeding: JEPTALNRECITAL 2018 5

Avec l{'}explosion du nombre de documents num{\'e}riques accessibles, les besoins en outils pour l{'}enrichissement s{\'e}mantique des donn{\'e}es textuelles, ainsi que des fonctionnalit{\'e}s avanc{\'e}es de recherche et d{'}exploration des collections, se font sentir. Cette combinaison entre les domaines de la recherche d{'}information et du traitement automatique des langues est l{'}une des caract{\'e}ristiques du projet N{\'e}onaute. Ce projet, financ{\'e} par la DGLFLF 1 en 2017 (appel Langues et num{\'e}rique), regroupe la Biblioth{\`e}que nationale de France (BnF), le LIPN - RCLN (CNRS UMR 7030) et l{'}Universit{\'e} de Strasbourg (LILPA, EA 1339). Son objectif principal est de doter les observateurs de la langue fran{\c{c}}aise d{'}un moteur de recherche s{'}appuyant sur une collection de sites de presse d{'}actualit{\'e}, collect{\'e}s automatiquement par la BnF au titre de sa mission de d{\'e}p{\^o}t l{\'e}gal de l{'}internet. Sur cette collection, le projet vise {\`a} proposer un moteur de recherche de nouvelle g{\'e}n{\'e}ration, disposant d{'}une indexation enrichie par l{'}analyse automatique des textes (analyse morphosyntaxique, entit{\'e}s nomm{\'e}es, th{\'e}matiques), d{'}une part, et d{'}outils de recherche, d{'}exploration et de visualisation multidimensionnelle interactive des r{\'e}sultats, d{'}autre part.
Une approche hybride pour la construction de lexiques bilingues d'expressions multi-mots \`a partir de corpus parall\`eles (A hybrid approach to build bilingual lexicons of multiword expressions from parallel corpora)
Date: 2017-06-01 00:00:00
Proceeding: JEPTALNRECITAL 2017 6

Les expressions multi-mots jouent un r{\^o}le important dans diff{\'e}rentes applications du Traitement Automatique de la Langue telles que la traduction automatique et la recherche d{'}information interlingue. Cet article, d{'}une part, d{\'e}crit une approche hybride pour l{'}acquisition d{'}un lexique bilingue d{'}expressions multi-mots {\`a} partir d{'}un corpus parall{\`e}le anglais-fran{\c{c}}ais, et d{'}autre part, pr{\'e}sente l{'}impact de l{'}utilisation d{'}un lexique bilingue sp{\'e}cialis{\'e} d{'}expressions multi-mots produit par cette approche sur les r{\'e}sultats du syst{\`e}me de traduction statistique libre Moses. Nous avons explor{\'e} deux m{\'e}triques bas{\'e}es sur la co-occurrence pour {\'e}valuer les liens d{'}alignement entre les expressions multi-mots des langues source et cible. Les r{\'e}sultats obtenus montrent que la m{\'e}trique utilisant un dictionnaire bilingue amorce de mots simples am{\'e}liore aussi bien la qualit{\'e} de l{'}alignement d{'}expressions multi-mots que celle de la traduction.
Dynamique phon\'etique et contr\^ole moteur dans la maladie de Parkinson: analyse du contr\^ole de la production des glides (Speech dynamics and motion control in people with Parkinson's disease: analysis of glides' production)
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Nous nous interrogeons quant {\`a} la possibilit{\'e} d{'}identifier les difficult{\'e}s de contr{\^o}le du mouvement chez les personnes atteintes de la maladie de Parkinson (MP) {\`a} partir de l{'}{\'e}tude de leurs comportements dans la production de sons de parole n{\'e}cessitant des mouvements continus des articulateurs supralaryng{\'e}s (logatomes VCV, o{\`u} C est un glide). Notre hypoth{\`e}se est que les parkinsoniens pr{\'e}sentent des modifications dans leur dynamique de mouvement par rapport {\`a} des personnes sans pathologie lors de la production. A cette fin, sont {\'e}tudi{\'e}s des sons de parole recueillis hors contexte communicationnel aupr{\`e}s de neuf personnes porteuses de la MP et de dix sujets sains. Les analyses r{\'e}v{\`e}lent des diff{\'e}rences entre les deux groupes, notamment en ce qui concerne l{'}espace articulatoire, l{'}amplitude des mouvements et leur localisation dans le plan F 1-F2. On note par ailleurs qu{'}un point-cible est pr{\'e}serv{\'e} lors de l{'}{\'e}mission de logatomes : le centre du glide.
Variabilit\'e des syllabes r\'ealis\'ees par des apprenants de l'anglais (Analysing syllable variability in a French learner corpus of English)
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Cette contribution analyse la segmentation syllabique des francophones du corpus d{'}apprenant d{'}anglais ANGLISH (Tortel 2009). A partir d{'}une m{\'e}thode d{'}alignement par alignement forc{\'e}, on montre la pertinence d{'}une analyse de l{'}interlangue fond{\'e}e sur la comparaison des dur{\'e}es des syllabes. La comparaison des r{\'e}alisations est ici centr{\'e}e sur une typologie des syllabes fond{\'e}e sur des propri{\'e}t{\'e}s distributionnelles, accentuelles et o{\`u} l{'}interlangue tient sa place (risques d{'}isosyllabicit{\'e} les plus manifestes pour les r{\'e}alisations des francophones). La variabilit{\'e} des r{\'e}alisations des syllabes est appr{\'e}ci{\'e}e en fonction des propri{\'e}t{\'e}s positionnelles, accentuelles et structurelles des syllabes. L{'}{\'e}tude d{\'e}montre l{'}int{\'e}r{\^e}t d{'}une approche fonctionnelle des syllabes, plus pertinente que les intervalles interconsonantiques et intervocaliques inspir{\'e}s de Ramus et al. (1999) pour la discrimination du niveau des locuteurs.
Recherche de « p\'erisegments » dans un contexte d'analyse conceptuelle assist\'ee par ordinateur : le concept d'« esprit » chez Peirce (Search of ``perisegments'' in computer-assisted conceptual analysis : the concept of ``mind'' in Peirce)
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

En sciences humaines et plus particuli{\`e}rement en philosophie, l{'}analyse conceptuelle (AC) est une pratique fondamentale qui permet de d{\'e}cortiquer les propri{\'e}t{\'e}s d{'}un concept. Lors de l{'}analyse d{'}un un corpus textuel, le principal d{\'e}fi est l{'}identification des segments de texte qui expriment le concept. Parfois, ces segments sont facilement reconnaissables gr{\^a}ce {\`a} une unit{\'e} lexicale attendue, appel{\'e}e forme canonique. Toutefois, ce n{'}est pas toujours le cas. Cet article propose une cha{\^\i}ne de traitement pour la d{\'e}couverte d{'}un certain nombre de segments p{\'e}riph{\'e}riques, dits p{\'e}risegments. Pour illustrer le processus, nous r{\'e}alisons des exp{\'e}rimentations sur le concept d{'}« esprit » dans les Collected Papers de Ch. S. Peirce, en obtenant une pr{\'e}cision moyenne sup{\'e}rieure {\`a} 83{\%}.
Predicting Multidimensional Subjective Ratings of Children' Readings from the Speech Signals for the Automatic Assessment of Fluency
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The objective of this research is to estimate multidimensional subjective ratings of the reading performance of young readers from signal-based objective measures. We here combine linguistic features (number of correct words, repetitions, deletions, insertions uttered per minute . . . ) with phonetic features. Expressivity is particularly difficult to predict since there is no unique golden standard. We here propose a novel framework for performing such an estimation that exploits multiple references performed by adults and demonstrate its efficiency using recordings of 273 pupils.
The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant{'}s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception.
Books of Hours. the First Liturgical Data Set for Text Segmentation.
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Handwritten Text Recognition, HTR, Optical Character Recognition, Optical Character Recognition (OCR), Text Segmentation

The Book of Hours was the bestseller of the late Middle Ages and Renaissance. It is a historical invaluable treasure, documenting the devotional practices of Christians in the late Middle Ages. Up to now, its textual content has been scarcely studied because of its manuscript nature, its length and its complex content. At first glance, it looks too standardized. However, the study of book of hours raises important challenges: (i) in image analysis, its often lavish ornamentation (illegible painted initials, line-fillers, etc.), abbreviated words, multilingualism are difficult to address in Handwritten Text Recognition (HTR); (ii) its hierarchical entangled structure offers a new field of investigation for text segmentation; (iii) in digital humanities, its textual content gives opportunities for historical analysis. In this paper, we provide the first corpus of books of hours, which consists of Latin transcriptions of 300 books of hours generated by Handwritten Text Recognition (HTR) - that is like Optical Character Recognition (OCR) but for handwritten and not printed texts. We designed a structural scheme of the book of hours and annotated manually two books of hours according to this scheme. Lastly, we performed a systematic evaluation of the main state of the art text segmentation approaches.
The CLARIN Knowledge Centre for Atypical Communication Expertise
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper introduces a new CLARIN Knowledge Center which is the K-Centre for Atypical Communication Expertise (ACE for short) which has been established at the Centre for Language and Speech Technology (CLST) at Radboud University. Atypical communication is an umbrella term used here to denote language use by second language learners, people with language disorders or those suffering from language disabilities, but also more broadly by bilinguals and users of sign languages. It involves multiple modalities (text, speech, sign, gesture) and encompasses different developmental stages. ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. We explain the mission of ACE and show its potential on a number of showcases and a use case.
Doctor Who? Framing Through Names and Titles in German
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. We investigate entity framing of political figures through the use of names and titles in German online discourse, enhancing current research in entity framing through titling and naming that concentrates on English only. We collect tweets that mention prominent German politicians and annotate them for stance. We find that the formality of naming in these tweets correlates positively with their stance. This confirms sociolinguistic observations that naming and titling can have a status-indicating function and suggests that this function is dominant in German tweets mentioning political figures. We also find that this status-indicating function is much weaker in tweets from users that are politically left-leaning than in tweets by right-leaning users. This is in line with observations from moral psychology that left-leaning and right-leaning users assign different importance to maintaining social hierarchies.
Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification, Negation

The sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class negation words like {``}no{''}, {``}not{''} or {``}without{''}, negation can also be caused by so-called polarity shifters. These are content words, such as verbs, nouns or adjectives, that shift polarities in their opposite direction, e.g. {``}abandoned{''} in {``}abandoned hope{''} or {``}alleviate{''} in {``}alleviate pain{''}. Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity. However, other shifters are restricted to a single shifting direction. {``}Recoup{''} shifts negative to positive in {``}recoup your losses{''}, but does not affect the positive polarity of {``}fortune{''} in {``}recoup a fortune{''}. Existing polarity shifter lexica only specify whether a word can, in general, cause shifting, but they do not specify when this is limited to one shifting direction. To address this issue we introduce a supervised classifier that determines the shifting direction of shifters. This classifier uses both resource-driven features, such as WordNet relations, and data-driven features like in-context polarity conflicts. Using this classifier we enhance the largest available polarity shifter lexicon.
Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Dependency Parsing, Sentence

Current sentence boundary detectors split documents into sequentially ordered sentences by detecting their beginnings and ends. Sentences, however, are more deeply structured even on this side of constituent and dependency structure: they can consist of a main sentence and several subordinate clauses as well as further segments (e.g. inserts in parentheses); they can even recursively embed whole sentences and then contain multiple sentence beginnings and ends. In this paper, we introduce a tool that segments sentences into tree structures to detect this type of recursive structure. To this end, we retrain different constituency parsers with the help of modified training data to transform them into sentence segmenters. With these segmenters, documents are mapped to sequences of sentence-related {``}logical document structures{''}. The resulting segmenters aim to improve downstream tasks by providing additional structural information. In this context, we experiment with German dependency parsing. We show that for certain sentence categories, which can be determined automatically, improvements in German dependency parsing can be achieved using our segmenter for preprocessing. The assumption suggests that improvements in other languages and tasks can be achieved.
Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation
Authors: Teruo M. Maruyama, Luiz S. Oliveira, Alceu S. Britto Jr, Robert Sabourin
Date: 2020-10-13 00:00:00

Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets.