Thomas François

Also published as: Thomas Francois


2024

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Exploring hybrid approaches to readability: experiments on the complementarity between linguistic features and transformers
Rodrigo Wilkens | Patrick Watrin | Rémi Cardon | Alice Pintard | Isabelle Gribomont | Thomas François
Findings of the Association for Computational Linguistics: EACL 2024

Linguistic features have a strong contribution in the context of the automatic assessment of text readability (ARA). They have been one of the anchors between the computational and theoretical models. With the development in the ARA field, the research moved to Deep Learning (DL). In an attempt to reconcile the mixed results reported in this context, we present a systematic comparison of 6 hybrid approaches along with standard Machine Learning and DL approaches, on 4 corpora (different languages and target audiences). The various experiments clearly highlighted two rather simple hybridization methods (soft label and simple concatenation). They also appear to be the most robust on smaller datasets and across various tasks and languages. This study stands out as the first to systematically compare different architectures and approaches to feature hybridization in DL, as well as comparing performance in terms of two languages and two target audiences of the text, which leads to a clearer pattern of results.

2023

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Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning
David Alfter | Elena Volodina | Thomas François | Arne Jönsson | Evelina Rennes
Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning

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Word Sense Disambiguation for Automatic Translation of Medical Dialogues into Pictographs
Magali Norré | Rémi Cardon | Vincent Vandeghinste | Thomas François
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Word sense disambiguation is an NLP task embedded in different applications. We propose to evaluate its contribution to the automatic translation of French texts into pictographs, in the context of communication between doctors and patients with an intellectual disability. Different general and/or medical language models (Word2Vec, fastText, CamemBERT, FlauBERT, DrBERT, and CamemBERT-bio) are tested in order to choose semantically correct pictographs leveraging the synsets in the French WordNets (WOLF and WoNeF). The results of our automatic evaluations show that our method based on Word2Vec and fastText significantly improves the precision of medical translations into pictographs. We also present an evaluation corpus adapted to this task.

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Annotation Linguistique pour l’Évaluation de la Simplification Automatique de Textes
Rémi Cardon | Adrien Bibal | Rodrigo Wilkens | David Alfter | Magali Norré | Adeline Müller | Patrick Watrin | Thomas François
Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 4 : articles déjà soumis ou acceptés en conférence internationale

L’évaluation des systèmes de simplification automatique de textes (SAT) est une tâche difficile, accomplie à l’aide de métriques automatiques et du jugement humain. Cependant, d’un point de vue linguistique, savoir ce qui est concrètement évalué n’est pas clair. Nous proposons d’annoter un des corpus de référence pour la SAT, ASSET, que nous utilisons pour éclaircir cette question. En plus de la contribution que constitue la ressource annotée, nous montrons comment elle peut être utilisée pour analyser le comportement de SARI, la mesure d’évaluation la plus populaire en SAT. Nous présentons nos conclusions comme une étape pour améliorer les protocoles d’évaluation en SAT à l’avenir.

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TCFLE-8: a Corpus of Learner Written Productions for French as a Foreign Language and its Application to Automated Essay Scoring
Rodrigo Wilkens | Alice Pintard | David Alfter | Vincent Folny | Thomas François
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Automated Essay Scoring (AES) aims to automatically assess the quality of essays. Automation enables large-scale assessment, improvements in consistency, reliability, and standardization. Those characteristics are of particular relevance in the context of language certification exams. However, a major bottleneck in the development of AES systems is the availability of corpora, which, unfortunately, are scarce, especially for languages other than English. In this paper, we aim to foster the development of AES for French by providing the TCFLE-8 corpus, a corpus of 6.5k essays collected in the context of the Test de Connaissance du Français (TCF - French Knowledge Test) certification exam. We report the strict quality procedure that led to the scoring of each essay by at least two raters according to the CEFR levels and to the creation of a balanced corpus. In addition, we describe how linguistic properties of the essays relate to the learners’ proficiency in TCFLE-8. We also advance the state-of-the-art performance for the AES task in French by experimenting with two strong baselines (i.e. RoBERTa and feature-based). Finally, we discuss the challenges of AES using TCFLE-8.

2022

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Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning
David Alfter | Elena Volodina | Thomas François | Piet Desmet | Frederik Cornillie | Arne Jönsson | Evelina Rennes
Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning

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Towards a Verb Profile: distribution of verbal tenses in FFL textbooks and in learner productions
Nami Yamaguchi | David Alfter | Kaori Sugiyama | Thomas François
Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning

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Is Attention Explanation? An Introduction to the Debate
Adrien Bibal | Rémi Cardon | David Alfter | Rodrigo Wilkens | Xiaoou Wang | Thomas François | Patrick Watrin
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The performance of deep learning models in NLP and other fields of machine learning has led to a rise in their popularity, and so the need for explanations of these models becomes paramount. Attention has been seen as a solution to increase performance, while providing some explanations. However, a debate has started to cast doubt on the explanatory power of attention in neural networks. Although the debate has created a vast literature thanks to contributions from various areas, the lack of communication is becoming more and more tangible. In this paper, we provide a clear overview of the insights on the debate by critically confronting works from these different areas. This holistic vision can be of great interest for future works in all the communities concerned by this debate. We sum up the main challenges spotted in these areas, and we conclude by discussing the most promising future avenues on attention as an explanation.

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FABRA: French Aggregator-Based Readability Assessment toolkit
Rodrigo Wilkens | David Alfter | Xiaoou Wang | Alice Pintard | Anaïs Tack | Kevin P. Yancey | Thomas François
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity, dependency counts and text coherence, while the most important predictors for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical sophistication. FABRA: have the potential to support new research on readability assessment for French.

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HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French
Amalia Todirascu | Rodrigo Wilkens | Eva Rolin | Thomas François | Delphine Bernhard | Núria Gala
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Reducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number of methods and evaluation campaigns haveemerged targeting lexical and syntactic transformations. In recent years, several studies exploit deep learning techniques basedon very large comparable corpora. Yet the lack of large amounts of corpora (original-simplified) for French has been hinderingthe development of an ATS tool for this language. In this paper, we present our system, which is based on a combination ofmethods relying on word embeddings for lexical simplification and rule-based strategies for syntax and discourse adaptations. We present an evaluation of the lexical, syntactic and discourse-level simplifications according to automatic and humanevaluations. We discuss the performances of our system at the lexical, syntactic, and discourse levels

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A Dictionary-Based Study of Word Sense Difficulty
David Alfter | Rémi Cardon | Thomas François
Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference

In this article, we present an exploratory study on perceived word sense difficulty by native and non-native speakers of French. We use a graded lexicon in conjunction with the French Wiktionary to generate tasks in bundles of four items. Annotators manually rate the difficulty of the word senses based on their usage in a sentence by selecting the easiest and the most difficult word sense out of four. Our results show that the native and non-native speakers largely agree when it comes to the difficulty of words. Further, the rankings derived from the manual annotation broadly follow the levels of the words in the graded resource, although these levels were not overtly available to annotators. Using clustering, we investigate whether there is a link between the complexity of a definition and the difficulty of the associated word sense. However, results were inconclusive. The annotated data set is available for research purposes.

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PADDLe: a Platform to Identify Complex Words for Learners of French as a Foreign Language (FFL)
Camille Pirali | Thomas François | Núria Gala
Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference

Annotations of word difficulty by readers provide invaluable insights into lexical complexity. Yet, there is currently a paucity of tools allowing researchers to gather such annotations in an adaptable and simple manner. This article presents PADDLe, an online platform aiming to fill that gap and designed to encourage best practices when collecting difficulty judgements. Studies crafted using the tool ask users to provide a selection of demographic information, then to annotate a certain number of texts and answer multiple-choice comprehension questions after each text. Researchers are encouraged to use a multi-level annotation scheme, to avoid the drawbacks of binary complexity annotations. Once a study is launched, its results are summarised in a visual representation accessible both to researchers and teachers, and can be downloaded in .csv format. Some findings of a pilot study designed with the tool are also provided in the article, to give an idea of the types of research questions it allows to answer.

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MWE for Essay Scoring English as a Foreign Language
Rodrigo Wilkens | Daiane Seibert | Xiaoou Wang | Thomas François
Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference

Mastering a foreign language like English can bring better opportunities. In this context, although multiword expressions (MWE) are associated with proficiency, they are usually neglected in the works of automatic scoring language learners. Therefore, we study MWE-based features (i.e., occurrence and concreteness) in this work, aiming at assessing their relevance for automated essay scoring. To achieve this goal, we also compare MWE features with other classic features, such as length-based, graded resource, orthographic neighbors, part-of-speech, morphology, dependency relations, verb tense, language development, and coherence. Although the results indicate that classic features are more significant than MWE for automatic scoring, we observed encouraging results when looking at the MWE concreteness through the levels.

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CENTAL at TSAR-2022 Shared Task: How Does Context Impact BERT-Generated Substitutions for Lexical Simplification?
Rodrigo Wilkens | David Alfter | Rémi Cardon | Isabelle Gribomont | Adrien Bibal | Watrin Patrick | Marie-Catherine De marneffe | Thomas François
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

Lexical simplification is the task of substituting a difficult word with a simpler equivalent for a target audience. This is currently commonly done by modeling lexical complexity on a continuous scale to identify simpler alternatives to difficult words. In the TSAR shared task, the organizers call for systems capable of generating substitutions in a zero-shot-task context, for English, Spanish and Portuguese. In this paper, we present the solution we (the cental team) proposed for the task. We explore the ability of BERT-like models to generate substitution words by masking the difficult word. To do so, we investigate various context enhancement strategies, that we combined into an ensemble method. We also explore different substitution ranking methods. We report on a post-submission analysis of the results and present our insights for potential improvements. The code for all our experiments is available at https://gitlab.com/Cental-FR/cental-tsar2022.

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L’Attention est-elle de l’Explication ? Une Introduction au Débat (Is Attention Explanation ? An Introduction to the Debate )
Adrien Bibal | Remi Cardon | David Alfter | Rodrigo Wilkens | Xiaoou Wang | Thomas François | Patrick Watrin
Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale

Nous présentons un résumé en français et un résumé en anglais de l’article Is Attention Explanation ? An Introduction to the Debate (Bibal et al., 2022), publié dans les actes de la conférence 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022).

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Investigating the Medical Coverage of a Translation System into Pictographs for Patients with an Intellectual Disability
Magali Norré | Vincent Vandeghinste | Thomas François | Bouillon Pierrette
Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

Communication between physician and patients can lead to misunderstandings, especially for disabled people. An automatic system that translates natural language into a pictographic language is one of the solutions that could help to overcome this issue. In this preliminary study, we present the French version of a translation system using the Arasaac pictographs and we investigate the strategies used by speech therapists to translate into pictographs. We also evaluate the medical coverage of this tool for translating physician questions and patient instructions.

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Linguistic Corpus Annotation for Automatic Text Simplification Evaluation
Rémi Cardon | Adrien Bibal | Rodrigo Wilkens | David Alfter | Magali Norré | Adeline Müller | Watrin Patrick | Thomas François
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Evaluating automatic text simplification (ATS) systems is a difficult task that is either performed by automatic metrics or user-based evaluations. However, from a linguistic point-of-view, it is not always clear on what bases these evaluations operate. In this paper, we propose annotations of the ASSET corpus that can be used to shed more light on ATS evaluation. In addition to contributing with this resource, we show how it can be used to analyze SARI’s behavior and to re-evaluate existing ATS systems. We present our insights as a step to improve ATS evaluation protocols in the future.

2021

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Extending a Text-to-Pictograph System to French and to Arasaac
Magali Norré | Vincent Vandeghinste | Pierrette Bouillon | Thomas François
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

We present an adaptation of the Text-to-Picto system, initially designed for Dutch, and extended to English and Spanish. The original system, aimed at people with an intellectual disability, automatically translates text into pictographs (Sclera and Beta). We extend it to French and add a large set of Arasaac pictographs linked to WordNet 3.1. To carry out this adaptation, we automatically link the pictographs and their metadata to synsets of two French WordNets and leverage this information to translate words into pictographs. We automatically and manually evaluate our system with different corpora corresponding to different use cases, including one for medical communication between doctors and patients. The system is also compared to similar systems in other languages.

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FrenLyS: A Tool for the Automatic Simplification of French General Language Texts
Eva Rolin | Quentin Langlois | Patrick Watrin | Thomas François
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture. To evaluate the different methods, a new evaluation dataset for French is introduced.

2020

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AMesure: A Web Platform to Assist the Clear Writing of Administrative Texts
Thomas François | Adeline Müller | Eva Rolin | Magali Norré
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

This article presents the AMesure platform, which aims to assist writers of French administrative texts in simplifying their writing. This platform includes a readability formula specialized for administrative texts and it also uses various natural language processing (NLP) tools to analyze texts and highlight a number of linguistic phenomena considered difficult to read. Finally, based on the difficulties identified, it offers pieces of advice coming from official plain language guides to users. This paper describes the different components of the system and reports an evaluation of these components.

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Text Simplification to Help Individuals with Low Vision Read More Fluently
Lauren Sauvan | Natacha Stolowy | Carlos Aguilar | Thomas François | Núria Gala | Frédéric Matonti | Eric Castet | Aurélie Calabrèse
Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)

The objective of this work is to introduce text simplification as a potential reading aid to help improve the poor reading performance experienced by visually impaired individuals. As a first step, we explore what makes a text especially complex when read with low vision, by assessing the individual effect of three word properties (frequency, orthographic similarity and length) on reading speed in the presence of Central visual Field Loss (CFL). Individuals with bilateral CFL induced by macular diseases read pairs of French sentences displayed with the self-paced reading method. For each sentence pair, sentence n contained a target word matched with a synonym word of the same length included in sentence n+1. Reading time was recorded for each target word. Given the corpus we used, our results show that (1) word frequency has a significant effect on reading time (the more frequent the faster the reading speed) with larger amplitude (in the range of seconds) compared to normal vision; (2) word neighborhood size has a significant effect on reading time (the more neighbors the slower the reading speed), this effect being rather small in amplitude, but interestingly reversed compared to normal vision; (3) word length has no significant effect on reading time. Supporting the development of new and more effective assistive technology to help low vision is an important and timely issue, with massive potential implications for social and rehabilitation practices. The end goal of this project will be to use our findings to custom text simplification to this specific population and use it as an optimal and efficient reading aid.

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Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words
Alice Pintard | Thomas François
Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)

Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).

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Alector: A Parallel Corpus of Simplified French Texts with Alignments of Misreadings by Poor and Dyslexic Readers
Núria Gala | Anaïs Tack | Ludivine Javourey-Drevet | Thomas François | Johannes C. Ziegler
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we present a new parallel corpus addressed to researchers, teachers, and speech therapists interested in text simplification as a means of alleviating difficulties in children learning to read. The corpus is composed of excerpts drawn from 79 authentic literary (tales, stories) and scientific (documentary) texts commonly used in French schools for children aged between 7 to 9 years old. The excerpts were manually simplified at the lexical, morpho-syntactic, and discourse levels in order to propose a parallel corpus for reading tests and for the development of automatic text simplification tools. A sample of 21 poor-reading and dyslexic children with an average reading delay of 2.5 years read a portion of the corpus. The transcripts of readings errors were integrated into the corpus with the goal of identifying lexical difficulty in the target population. By means of statistical testing, we provide evidence that the manual simplifications significantly reduced reading errors, highlighting that the words targeted for simplification were not only well-chosen but also substituted with substantially easier alternatives. The entire corpus is available for consultation through a web interface and available on demand for research purposes.

2019

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PolylexFLE : une base de données d’expressions polylexicales pour le FLE (PolylexFLE : a database of multiword expressions for French L2 language learning)
Amalia Todirascu | Marion Cargill | Thomas Francois
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs

Nous présentons la base PolylexFLE, contenant 4295 expressions polylexicales. Elle est integrée dans une plateforme d’apprentissage du FLE, SimpleApprenant, destinée à l’apprentissage des expressions polylexicales verbales (idiomatiques, collocations ou expressions figées). Afin de proposer des exercices adaptés au niveau du Cadre européen de référence pour les langues (CECR), nous avons utilisé une procédure mixte (manuelle et automatique) pour annoter 1098 expressions selon les niveaux de compétence du CECR. L’article se concentre sur la procédure automatique qui identifie, dans un premier temps, les expressions de la base PolylexFLE dans un corpus à l’aide d’un système à base d’expressions régulières. Dans un second temps, leur distribution au sein de corpus, annoté selon l’échelle du CECR, est estimée et transformée en un niveau CECR unique.

2018

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NT2Lex: A CEFR-Graded Lexical Resource for Dutch as a Foreign Language Linked to Open Dutch WordNet
Anaïs Tack | Thomas François | Piet Desmet | Cédrick Fairon
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we introduce NT2Lex, a novel lexical resource for Dutch as a foreign language (NT2) which includes frequency distributions of 17,743 words and expressions attested in expert-written textbook texts and readers graded along the scale of the Common European Framework of Reference (CEFR). In essence, the lexicon informs us about what kind of vocabulary should be understood when reading Dutch as a non-native reader at a particular proficiency level. The main novelty of the resource with respect to the previously developed CEFR-graded lexicons concerns the introduction of corpus-based evidence for L2 word sense complexity through the linkage to Open Dutch WordNet (Postma et al., 2016). The resource thus contains, on top of the lemmatised and part-of-speech tagged lexical entries, a total of 11,999 unique word senses and 8,934 distinct synsets.

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The Interface Between Readability and Automatic Text Simplification
Thomas François
Proceedings of the 1st Workshop on Automatic Text Adaptation (ATA)

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Assisted Lexical Simplification for French Native Children with Reading Difficulties
Firas Hmida | Mokhtar B. Billami | Thomas François | Núria Gala
Proceedings of the 1st Workshop on Automatic Text Adaptation (ATA)

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ReSyf: a French lexicon with ranked synonyms
Mokhtar B. Billami | Thomas François | Núria Gala
Proceedings of the 27th International Conference on Computational Linguistics

In this article, we present ReSyf, a lexical resource of monolingual synonyms ranked according to their difficulty to be read and understood by native learners of French. The synonyms come from an existing lexical network and they have been semantically disambiguated and refined. A ranking algorithm, based on a wide range of linguistic features and validated through an evaluation campaign with human annotators, automatically sorts the synonyms corresponding to a given word sense by reading difficulty. ReSyf is freely available and will be integrated into a web platform for reading assistance. It can also be applied to perform lexical simplification of French texts.

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EFLLex: A Graded Lexical Resource for Learners of English as a Foreign Language
Luise Dürlich | Thomas François
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Human and Automated CEFR-based Grading of Short Answers
Anaïs Tack | Thomas François | Sophie Roekhaut | Cédrick Fairon
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

This paper is concerned with the task of automatically assessing the written proficiency level of non-native (L2) learners of English. Drawing on previous research on automated L2 writing assessment following the Common European Framework of Reference for Languages (CEFR), we investigate the possibilities and difficulties of deriving the CEFR level from short answers to open-ended questions, which has not yet been subjected to numerous studies up to date. The object of our study is twofold: to examine the intricacy involved with both human and automated CEFR-based grading of short answers. On the one hand, we describe the compilation of a learner corpus of short answers graded with CEFR levels by three certified Cambridge examiners. We mainly observe that, although the shortness of the answers is reported as undermining a clear-cut evaluation, the length of the answer does not necessarily correlate with inter-examiner disagreement. On the other hand, we explore the development of a soft-voting system for the automated CEFR-based grading of short answers and draw tentative conclusions about its use in a computer-assisted testing (CAT) setting.

2016

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Bleu, contusion, ecchymose : tri automatique de synonymes en fonction de leur difficulté de lecture et compréhension (Automatic ranking of synonyms according to their reading and comprehension difficulty)
Thomas Francois | Mokhtar B. Billami | Núria Gala | Delphine Bernhard
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)

La lisibilité d’un texte dépend fortement de la difficulté des unités lexicales qui le composent. La simplification lexicale vise ainsi à remplacer les termes complexes par des équivalents sémantiques plus simples à comprendre : par exemple, BLEU (‘résultat d’un choc’) est plus simple que CONTUSION ou ECCHYMOSE. Il est pour cela nécessaire de disposer de ressources qui listent des synonymes pour des sens donnés et les trient par ordre de difficulté. Cet article décrit une méthode pour constituer une ressource de ce type pour le français. Les listes de synonymes sont extraites de BabelNet et de JeuxDeMots, puis triées grâce à un algorithme statistique d’ordonnancement. Les résultats du tri sont évalués par rapport à 36 listes de synonymes ordonnées manuellement par quarante annotateurs.

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Modèles adaptatifs pour prédire automatiquement la compétence lexicale d’un apprenant de français langue étrangère (Adaptive models for automatically predicting the lexical competence of French as a foreign language learners)
Anaïs Tack | Thomas François | Anne-Laure Ligozat | Cédrick Fairon
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)

Cette étude examine l’utilisation de méthodes d’apprentissage incrémental supervisé afin de prédire la compétence lexicale d’apprenants de français langue étrangère (FLE). Les apprenants ciblés sont des néerlandophones ayant un niveau A2/B1 selon le Cadre européen commun de référence pour les langues (CECR). À l’instar des travaux récents portant sur la prédiction de la maîtrise lexicale à l’aide d’indices de complexité, nous élaborons deux types de modèles qui s’adaptent en fonction d’un retour d’expérience, révélant les connaissances de l’apprenant. En particulier, nous définissons (i) un modèle qui prédit la compétence lexicale de tous les apprenants du même niveau de maîtrise et (ii) un modèle qui prédit la compétence lexicale d’un apprenant individuel. Les modèles obtenus sont ensuite évalués par rapport à un modèle de référence déterminant la compétence lexicale à partir d’un lexique spécialisé pour le FLE et s’avèrent gagner significativement en exactitude (9%-17%).

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Classification automatique de dictées selon leur niveau de difficulté de compréhension et orthographique (Automatic classification of dictations according to their complexity for comprehension and writing production)
Adeline Müller | Thomas Francois | Sophie Roekhaut | Cedrick Fairon
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)

Cet article présente une approche visant à évaluer automatiquement la difficulté de dictées en vue de les intégrer dans une plateforme d’apprentissage de l’orthographe. La particularité de l’exercice de la dictée est de devoir percevoir du code oral et de le retranscrire via le code écrit. Nous envisageons ce double niveau de difficulté à l’aide de 375 variables mesurant la difficulté de compréhension d’un texte ainsi que les phénomènes orthographiques et grammaticaux complexes qu’il contient. Un sous-ensemble optimal de ces variables est combiné à l’aide d’un modèle par machines à vecteurs de support (SVM) qui classe correctement 56% des textes. Les variables lexicales basées sur la liste orthographique de Catach (1984) se révèlent les plus informatives pour le modèle.

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SVALex: a CEFR-graded Lexical Resource for Swedish Foreign and Second Language Learners
Thomas François | Elena Volodina | Ildikó Pilán | Anaïs Tack
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The paper introduces SVALex, a lexical resource primarily aimed at learners and teachers of Swedish as a foreign and second language that describes the distribution of 15,681 words and expressions across the Common European Framework of Reference (CEFR). The resource is based on a corpus of coursebook texts, and thus describes receptive vocabulary learners are exposed to during reading activities, as opposed to productive vocabulary they use when speaking or writing. The paper describes the methodology applied to create the list and to estimate the frequency distribution. It also discusses some characteristics of the resulting resource and compares it to other lexical resources for Swedish. An interesting feature of this resource is the possibility to separate the wheat from the chaff, identifying the core vocabulary at each level, i.e. vocabulary shared by several coursebook writers at each level, from peripheral vocabulary which is used by the minority of the coursebook writers.

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Evaluating Lexical Simplification and Vocabulary Knowledge for Learners of French: Possibilities of Using the FLELex Resource
Anaïs Tack | Thomas François | Anne-Laure Ligozat | Cédrick Fairon
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This study examines two possibilities of using the FLELex graded lexicon for the automated assessment of text complexity in French as a foreign language learning. From the lexical frequency distributions described in FLELex, we derive a single level of difficulty for each word in a parallel corpus of original and simplified texts. We then use this data to automatically address the lexical complexity of texts in two ways. On the one hand, we evaluate the degree of lexical simplification in manually simplified texts with respect to their original version. Our results show a significant simplification effect, both in the case of French narratives simplified for non-native readers and in the case of simplified Wikipedia texts. On the other hand, we define a predictive model which identifies the number of words in a text that are expected to be known at a particular learning level. We assess the accuracy with which these predictions are able to capture actual word knowledge as reported by Dutch-speaking learners of French. Our study shows that although the predictions seem relatively accurate in general (87.4% to 92.3%), they do not yet seem to cover the learners’ lack of knowledge very well.

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Combining Manual and Automatic Prosodic Annotation for Expressive Speech Synthesis
Sandrine Brognaux | Thomas François | Marco Saerens
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Text-to-speech has long been centered on the production of an intelligible message of good quality. More recently, interest has shifted to the generation of more natural and expressive speech. A major issue of existing approaches is that they usually rely on a manual annotation in expressive styles, which tends to be rather subjective. A typical related issue is that the annotation is strongly influenced ― and possibly biased ― by the semantic content of the text (e.g. a shot or a fault may incite the annotator to tag that sequence as expressing a high degree of excitation, independently of its acoustic realization). This paper investigates the assumption that human annotation of basketball commentaries in excitation levels can be automatically improved on the basis of acoustic features. It presents two techniques for label correction exploiting a Gaussian mixture and a proportional-odds logistic regression. The automatically re-annotated corpus is then used to train HMM-based expressive speech synthesizers, the performance of which is assessed through subjective evaluations. The results indicate that the automatic correction of the annotation with Gaussian mixture helps to synthesize more contrasted excitation levels, while preserving naturalness.

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Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)
Dominique Brunato | Felice Dell’Orletta | Giulia Venturi | Thomas François | Philippe Blache
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

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SweLLex: Second language learners’ productive vocabulary
Elena Volodina | Ildikó Pilán | Lorena Llozhi | Baptiste Degryse | Thomas François
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

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Are Cohesive Features Relevant for Text Readability Evaluation?
Amalia Todirascu | Thomas François | Delphine Bernhard | Núria Gala | Anne-Laure Ligozat
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper investigates the effectiveness of 65 cohesion-based variables that are commonly used in the literature as predictive features to assess text readability. We evaluate the efficiency of these variables across narrative and informative texts intended for an audience of L2 French learners. In our experiments, we use a French corpus that has been both manually and automatically annotated as regards to co-reference and anaphoric chains. The efficiency of the 65 variables for readability is analyzed through a correlational analysis and some modelling experiments.

2014

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Syntactic Sentence Simplification for French
Laetitia Brouwers | Delphine Bernhard | Anne-Laure Ligozat | Thomas François
Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)

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An analysis of a French as a Foreign Language Corpus for Readability Assessment
Thomas François
Proceedings of the third workshop on NLP for computer-assisted language learning

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A model to predict lexical complexity and to grade words (Un modèle pour prédire la complexité lexicale et graduer les mots) [in French]
Núria Gala | Thomas François | Delphine Bernhard | Cédrick Fairon
Proceedings of TALN 2014 (Volume 1: Long Papers)

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AMesure: a readability formula for administrative texts (AMESURE: une plateforme de lisibilité pour les textes administratifs) [in French]
Thomas François | Laetitia Brouwers | Hubert Naets | Cédrick Fairon
Proceedings of TALN 2014 (Volume 2: Short Papers)

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FLELex: a graded lexical resource for French foreign learners
Thomas François | Nùria Gala | Patrick Watrin | Cédrick Fairon
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present FLELex, the first graded lexicon for French as a foreign language (FFL) that reports word frequencies by difficulty level (according to the CEFR scale). It has been obtained from a tagged corpus of 777,000 words from available textbooks and simplified readers intended for FFL learners. Our goal is to freely provide this resource to the community to be used for a variety of purposes going from the assessment of the lexical difficulty of a text, to the selection of simpler words within text simplification systems, and also as a dictionary in assistive tools for writing.

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Multiple Choice Question Corpus Analysis for Distractor Characterization
Van-Minh Pho | Thibault André | Anne-Laure Ligozat | Brigitte Grau | Gabriel Illouz | Thomas François
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we present a study of MCQ aiming to define criteria in order to automatically select distractors. We are aiming to show that distractor editing follows rules like syntactic and semantic homogeneity according to associated answer, and the possibility to automatically identify this homogeneity. Manual analysis shows that homogeneity rule is respected to edit distractors and automatic analysis shows the possibility to reproduce these criteria. These ones can be used in future works to automatically select distractors, with the combination of other criteria.

2013

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Automatic extraction of contextual valence shifters.
Noémi Boubel | Thomas François | Hubert Naets
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Les apports du TAL à la lisibilité du français langue étrangère [Contributions of NLP to the readability of French as a foreign language]
Thomas François | Cédrick Fairon
Traitement Automatique des Langues, Volume 54, Numéro 1 : Varia [Varia]

2012

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Simplification syntaxique de phrases pour le français (Syntactic Simplification for French Sentences) [in French]
Laetitia Brouwers | Delphine Bernhard | Anne-Laure Ligozat | Thomas François
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

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An “AI readability” Formula for French as a Foreign Language
Thomas François | Cédrick Fairon
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Do NLP and machine learning improve traditional readability formulas?
Thomas François | Eleni Miltsakaki
Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations

2011

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An N-gram Frequency Database Reference to Handle MWE Extraction in NLP Applications
Patrick Watrin | Thomas François
Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World

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Quel apport des unités polylexicales dans une formule de lisibilité pour le français langue étrangère (What is the contribution of multi-word expressions in a readability formula for the French as a foreign language)
Thomas François | Patrick Watrin
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Cette étude envisage l’emploi des unités polylexicales (UPs) comme prédicteurs dans une formule de lisibilité pour le français langue étrangère. À l’aide d’un extracteur d’UPs combinant une approche statistique à un filtre linguistique, nous définissons six variables qui prennent en compte la densité et la probabilité des UPs nominales, mais aussi leur structure interne. Nos expérimentations concluent à un faible pouvoir prédictif de ces six variables et révèlent qu’une simple approche basée sur la probabilité moyenne des n-grammes des textes est plus efficace.

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On the Contribution of MWE-based Features to a Readability Formula for French as a Foreign Language
Thomas François | Patrick Watrin
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2009

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Modèles statistiques pour l’estimation automatique de la difficulté de textes de FLE
Thomas François
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. REncontres jeunes Chercheurs en Informatique pour le Traitement Automatique des Langues

La lecture constitue l’une des tâches essentielles dans l’apprentissage d’une langue étrangère. Toutefois, la découverte d’un texte portant sur un sujet précis et qui soit adapté au niveau de chaque apprenant est consommatrice de temps et pourrait être automatisée. Des expériences montrent que, pour l’anglais, l’utilisation de classifieurs statistiques permet d’estimer automatiquement la difficulté d’un texte. Dans cet article, nous proposons une méthodologie originale comparant, pour le français langue étrangère (FLE), diverses techniques de classification (la régression logistique, le bagging et le boosting) sur deux corpus d’entraînement. Il ressort de cette analyse comparative une légère supériorité de la régression logistique multinomiale.

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Combining a Statistical Language Model with Logistic Regression to Predict the Lexical and Syntactic Difficulty of Texts for FFL
Thomas François
Proceedings of the Student Research Workshop at EACL 2009