Papers by Gwénolé Lecorvé
Mama/Papa, Is this Text for Me? (2020.coling-main)
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Rashedur Rahman, Gwénolé Lecorvé, Aline Étienne, Delphine Battistelli, Nicolas Béchet, Jonathan Chevelu
| Challenge: | Existing methods to predict minimal age from which text can be understood for children are unresolved in computational linguistics. |
| Approach: | They propose a method which predicts the minimum age from which a text can be understood by a recurrent neural network. |
| Outcome: | The proposed method outperforms state-of-the-art models at sentence and text levels and achieves mean absolute errors of 1.86 and 2.28. |
Style versus Content: A distinction without a (learnable) difference? (2020.coling-main)
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| Challenge: | Textual style transfer assumes that it is possible to separate style from content . however, style transfer can provide insight into language more generally . |
| Approach: | They propose to use sentiment transfer to examine whether style transfer is possible . they employ adversarial encoder-decoder networks to analyze style-related features . |
| Outcome: | The proposed method combines style transfer with content preservation and fluency to show that style cannot be usefully separated from content within style transfer systems. |
DivMerge: A divergence-based model merging method for multi-tasking (2026.eacl-long)
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| Challenge: | Existing methods for multitask learning struggle with interference between tasks, especially as the number of tasks grows. |
| Approach: | They propose a reference-free method that minimizes the divergence between models' outputs and those of the merged model, automatically balancing task importance. |
| Outcome: | The proposed method outperforms existing methods on classification and generative tasks and remains robust when scaling to more tasks. |
Factual Knowledge Assessment of Language Models Using Distractors (2025.coling-main)
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Hichem Ammar Khodja, Abderrahmane Ait gueni ssaid, Frederic Bechet, Quentin Brabant, Alexis Nasr, Gwénolé Lecorvé
| Challenge: | Language models encode extensive factual knowledge within their parameters. |
| Approach: | They propose a new interpretable knowledge assessment method that leverages distractors to provide incorrect alternatives to the correct answer. |
| Outcome: | The proposed method shows that it is aligned with human judgment and stronger robustness to verbalization artifacts. |
Age Recommendation for Texts (2020.lrec-1)
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| Challenge: | adequacy of a text’s characteristics with the person’s capacities and knowledge is critical in the case of . a child since her/his cognitive and linguistic skills are still under development. |
| Approach: | They propose a natural language processing task which consists in predicting the age from which a text can be understood by someone. |
| Outcome: | The proposed model outperforms psycholinguist models on a French text dataset and shows that the results are more accurate than psycholingual models. |
WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models (2024.lrec-main)
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| Challenge: | Factual update is a task of inserting, replacing, or removing facts in large language models. |
| Approach: | They present a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static. |
| Outcome: | The proposed dataset compares the state of the Wikidata knowledge base at 4 January 2021 and 27 February 2023. |
KGConv, a Conversational Corpus Grounded in Wikidata (2024.lrec-main)
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| Challenge: | a large corpus of 71k English conversations contains on average 8.6 questions . Unlike open domain and task-oriented dialogues, information seeking conversations are driven by the desire to acquire or evaluate knowledge. |
| Approach: | They propose a large corpus of 71k English conversations where each question-answer pair is grounded in a Wikidata fact. |
| Outcome: | The proposed dataset can be used for knowledge-based, conversational question generation . it can also be used to generate single-turn questions from Wikidata triples, question rewriting, question answering from conversation or knowledge graphs and quiz generation. |
SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (2022.aacl-main)
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| Challenge: | a paper focuses on the generation of natural language questions based on SPARQL queries . knowledge-based approaches have become popular in the field of question answering and dialogue . |
| Approach: | This paper focuses on the generation of natural language questions based on SPARQL queries . it uses 4 knowledge-based QA corpora homogenized for the task and a new challenge set is introduced . |
| Outcome: | The proposed task is based on the generation of questions in a conversational context. |
CoQAR: Question Rewriting on CoQA (2022.lrec-1)
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| Challenge: | Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult. |
| Approach: | They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models . |
| Outcome: | The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. |
Statistical Deficiency for Task Inclusion Estimation (2025.acl-long)
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Loïc Fosse, Frederic Bechet, Benoit Favre, Géraldine Damnati, Gwénolé Lecorvé, Maxime Darrin, Philippe Formont, Pablo Piantanida
| Challenge: | Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. |
| Approach: | They propose a theoretically grounded setup to define the notion of task and compute the inclusion between two tasks from a statistical deficiency point of view. |
| Outcome: | The proposed model estimates the degree of inclusion between tasks on synthetic data and reconstructs the classic NLP pipeline. |
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)
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| Challenge: | Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types. |
| Approach: | They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french. |
| Outcome: | The proposed method clarifies the main concepts implied by the analysis of emotions as they are expressed in texts and performs a manual annotation campaign on a corpus of 1,594 texts (ca. 515K tokens) of different genres. |