Papers by Olivier Ferret
“Women do not have heart attacks!” Gender Biases in Automatically Generated Clinical Cases in French (2025.findings-naacl)
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| Challenge: | Healthcare professionals are increasingly including Language Models (LMs) in clinical practice. |
| Approach: | They propose to use LMs to generate clinical cases in french and an automatic linguistic gender detection tool to measure gender biases. |
| Outcome: | The proposed model over-generates cases describing male patients, creating synthetic corpora that are not consistent with documented prevalence for these disorders. |
Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri (2020.lrec-1)
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| Challenge: | Using a frequency-based method, we can identify subsets of the same word contexts without any reference data. |
| Approach: | They compare 11 different French dependency parsers on a specialized corpus to generate distributional thesauri using a frequency-based method. |
| Outcome: | The proposed method can identify relevant subsets without reference data and the similarity is confirmed on a restricted distributional benchmark. |
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition (P19-2)
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| Challenge: | Off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. |
| Approach: | They combine off-the-shelf contextual embeddings with static word2vec embedders trained on a small in-domain corpus built from task data to reach and sometimes outperform representations learned from a large corpus in the medical domain. |
| Outcome: | The proposed embedding strategies outperform representations learned from a large corpus in the medical domain. |
Few-Shot Event Argument Extraction Based on a Meta-Learning Approach (2024.naacl-srw)
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| Challenge: | Recent studies on few-shot event extraction focus on event trigger detection and argument extraction in meta-learning contexts. |
| Approach: | They propose to use prototypical networks to perform few-shot event argument extraction . they propose to inject syntactic knowledge into the model to enhance relation embeddings . |
| Outcome: | The proposed approach achieves strong performance on ACE 2005 in several few-shot configurations. |
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
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Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023.eacl-main)
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| Challenge: | In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. |
| Approach: | They use auto-regressive neural models to generate a clinical case corpus annotated with clinical entities and evaluate it for a named entity recognition task. |
| Outcome: | The proposed model can produce clinical case corpus annotated with clinical entities while maintaining confidentiality. |
Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)
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| Challenge: | Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base. |
| Approach: | They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task. |
| Outcome: | The proposed method builds annotated datasets of tweets with ambiguous mentions and a Twitter KB defining the entities. |
Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
| Outcome: | The proposed method can be used to build word or type embeddings from contextual models . it can be exploited for a wide set of English nouns, showing it can improve distributional thesauri . |
Re-train or Train from Scratch? Comparing Pre-training Strategies of BERT in the Medical Domain (2022.lrec-1)
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| Challenge: | Recent years have witnessed the widespread use of transfer learning techniques in Natural Language Processing (NLP) |
| Approach: | They train BERT models from scratch using many configurations involving general and medical corpora. |
| Outcome: | The initial corpus only has a weak influence when these are further pre-trained on a medical corpus. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop? (2022.coling-1)
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| Challenge: | Recent developments have shown that pre-trained language models are effective soft reasoners over language. |
| Approach: | They propose to model multi-hop reasoning process as a sequence of explicit single-hop steps. |
| Outcome: | The proposed model improves on multiple-choice question answering and reading comprehension with 68.4% and 16.0% w.r.t. classic PLMs. |
CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives (2022.lrec-1)
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| Challenge: | Modern Natural Language Processing relies on the availability of annotated corpora for training and evaluation. |
| Approach: | They propose to annotate sentences in French using a definition of similarity guided by clinical facts and use it to evaluate the corpus. |
| Outcome: | The proposed model can capture similarity with state-of-the-art performance on the DEFT STS shared task evaluation data set. |
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters (2020.coling-main)
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Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, Hiroshi Noji, Pierre Zweigenbaum, Jun’ichi Tsujii
| Challenge: | Pre-trained language representations from Transformers have become the most popular choice for building NLP systems. |
| Approach: | They propose a new variant of BERT that drops the wordpiece tokenization system altogether . they propose 'characterBERT' module to represent entire words by consulting their characters . |
| Outcome: | The proposed model improves performance on a variety of medical domain tasks while producing robust, word-level, and open-vocabulary representations. |
Using pseudo-senses for improving the extraction of synonyms from word embeddings (P18-2)
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| Challenge: | Existing methods for specializing word embeddings rely on external knowledge . we propose a new method that focuses on semantic similarity and synonym extraction without external knowledge. |
| Approach: | They propose a method that exploits pseudo-sense for building several representations for each word and uses these representations to make initial embeddings more generic. |
| Outcome: | The proposed method improves word embeddings without external knowledge and focuses on semantic similarity and synonym extraction. |
Language Models and Semantic Relations: A Dual Relationship (2024.lrec-main)
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| Challenge: | Existing studies on language models for the extraction of semantic relations have focused on injecting semantic knowledge into these models to enhance them. |
| Approach: | They propose to extract lexical semantic relations from a BERT model and inject them into it using unsupervised methods based on semantic similarity at word and sentence levels. |
| Outcome: | The proposed method allows to enrich a BERT model without using any external semantic resource. |
Multi-Level Information Retrieval Augmented Generation for Knowledge-based Visual Question Answering (2024.emnlp-main)
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| Challenge: | Knowledge-Aware Visual Question Answering about Entity tasks require two separate steps to generate accurate answers. |
| Approach: | They propose a multi-level information RAG approach that enhances answer generation through entity retrieval and query expansion. |
| Outcome: | The proposed approach improves answer generation through entity retrieval and query expansion. |