Papers by Frederic Bechet
Factual Knowledge Assessment of Language Models Using Distractors (2025.coling-main)
Copied to clipboard
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. |
WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models (2024.lrec-main)
Copied to clipboard
| 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. |
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |
Question Generation and Answering for exploring Digital Humanities collections (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent advances in representation learning of text have achieved impressive results on benchmark Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a question answering paradigm that uses a BART Transformer based generative model to generate question data. |
| Outcome: | The proposed approach is validated on a new corpus of digitized archive collections of a French Social Science journal. |
Adding Syntactic Annotations to Flickr30k Entities Corpus for Multimodal Ambiguous Prepositional-Phrase Attachment Resolution (L18-1)
Copied to clipboard
| Challenge: | Using visual features extracted from an image, we propose to study the joint processing of image and language features for the Preposition-Phrase attachment disambiguation task. |
| Approach: | They propose to add syntactic annotations to the captions of the Flickr30k Entities corpus to study the joint processing of image and language features for the Preposition-Phrase attachment disambiguation task. |
| Outcome: | The proposed framework is based on the captions of the Flickr30k Entities corpus and is automatically projected on their French and German translations. |
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations (D19-58)
Copied to clipboard
Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan
| Challenge: | Recent large corpora of triplets have opened the door to supervised machine learning approaches for Question-Answering. |
| Approach: | They propose to generate questions from the semantic Frame analysis of large corpora using a CALOR-QUEST resource in French and use it to improve machine reading comprehension. |
| Outcome: | The proposed method generates questions from the semantic Frame analysis of large corpora and then tests them on the CALOR-QUEST resource in French. |
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora (2020.lrec-1)
Copied to clipboard
| Challenge: | a recent study has shown that language mismatch and domain mismatch can affect performance of a machine reading task . a factor between language mismatched and domain-mismatched has the strongest influence on performance . |
| Approach: | They compare the cross-language and cross-domain capabilities of BERT on a machine reading comprehension task on two corpora: SQuAD and a new French Machine Reading dataset. |
| Outcome: | The proposed model matches human performance on a machine reading comprehension task with BERT on Chinese and French documents with interesting results. |
Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models (2025.coling-main)
Copied to clipboard
| Challenge: | a study examines the behavior of routers in Mixture of Experts (MoE) models . experts with similar linguistic traits are often routed to the same expert regardless of context . |
| Approach: | They investigate how tokens are routed based on their linguistic features . they aim to explore whether experts specialize in processing tokens with similar linguistic traits . |
| Outcome: | The proposed model-integrated routers are based on Mixture of Experts (MoE) models . the results show that expert specialization is high for POS categories . |
Semantic Frame Parsing for Information Extraction : the CALOR corpus (L18-1)
Copied to clipboard
| Challenge: | a recent study compares the semantic parsing of encyclopedic history texts with the Berkeley FrameNet project. |
| Approach: | They propose to use Berkeley FrameNet to parse encyclopedic history texts . they use a sequence labeling model which optimizes frame identification and role segmentation . |
| Outcome: | The proposed approach leverages the manual annotation of larger corpora than full text parsing. |
A linguistically-motivated evaluation methodology for unraveling model’s abilities in reading comprehension tasks (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing models fail for linguistic characteristics of input examples, despite the impressive quantity of scientific studies dedicated to them, the capabilities, limitations, and risks of these models remain largely unknown. |
| Approach: | They propose to use semantic frame annotation to characterize examples by a small number of complexity factors to account for model’s difficulty. |
| Outcome: | The proposed evaluation methodology is based on the intuition that certain examples consistently yield lower scores regardless of model size or architecture. |
Statistical Deficiency for Task Inclusion Estimation (2025.acl-long)
Copied to clipboard
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. |