Lorraine Goeuriot


2024

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Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains
Vincent Segonne | Aidan Mannion | Laura Cristina Alonzo Canul | Alexandre Daniel Audibert | Xingyu Liu | Cécile Macaire | Adrien Pupier | Yongxin Zhou | Mathilde Aguiar | Felix E. Herron | Magali Norré | Massih R Amini | Pierrette Bouillon | Iris Eshkol-Taravella | Emmanuelle Esperança-Rodier | Thomas François | Lorraine Goeuriot | Jérôme Goulian | Mathieu Lafourcade | Benjamin Lecouteux | François Portet | Fabien Ringeval | Vincent Vandeghinste | Maximin Coavoux | Marco Dinarelli | Didier Schwab
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Pretrained Language Models (PLMs) are the de facto backbone of most state-of-the-art NLP systems. In this paper, we introduce a family of domain-specific pretrained PLMs for French, focusing on three important domains: transcribed speech, medicine, and law. We use a transformer architecture based on efficient methods (LinFormer) to maximise their utility, since these domains often involve processing long documents. We evaluate and compare our models to state-of-the-art models on a diverse set of tasks and datasets, some of which are introduced in this paper. We gather the datasets into a new French-language evaluation benchmark for these three domains. We also compare various training configurations: continued pretraining, pretraining from scratch, as well as single- and multi-domain pretraining. Extensive domain-specific experiments show that it is possible to attain competitive downstream performance even when pre-training with the approximative LinFormer attention mechanism. For full reproducibility, we release the models and pretraining data, as well as contributed datasets.

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MedDialog-FR: A French Version of the MedDialog Corpus for Multi-label Classification and Response Generation Related to Women’s Intimate Health
Xingyu Liu | Vincent Segonne | Aidan Mannion | Didier Schwab | Lorraine Goeuriot | François Portet
Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024

This article presents MedDialog-FR, a large publicly available corpus of French medical conversations for the medical domain. Motivated by the lack of French dialogue corpora for data-driven dialogue systems and the paucity of available information related to women’s intimate health, we introduce an annotated corpus of question-and-answer dialogues between a real patient and a real doctor concerning women’s intimate health. The corpus is composed of about 20,000 dialogues automatically translated from the English version of MedDialog-EN. The corpus test set is composed of 1,400 dialogues that have been manually post-edited and annotated with 22 categories from the UMLS ontology. We also fine-tuned state-of-the-art reference models to automatically perform multi-label classification and response generation to give an initial performance benchmark and highlight the difficulty of the tasks.

2023

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Augmentation des modèles de langage français par graphes de connaissances pour la reconnaissance des entités biomédicales
Aidan Mannion | Schwab Didier | Lorraine Goeuriot | Thierry Chevalier
Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs

Des travaux récents dans le domaine du traitement du langage naturel ont démontré l’efficacité des modèles de langage pré-entraînés pour une grande variété d’applications générales. Les modèles de langage à grande échelle acquièrent généralement ces capacités en modélisant la distribution statistique des mots par un apprentissage auto-supervisé sur de grandes quantités de texte. Toutefois, pour les domaines spécialisés à faibles ressources, tels que le traitement de documents cliniques, en particulier dans des langues autres que l’anglais, la nécessité d’intégrer des connaissances structurées reste d’une grande importance. Cet article se concentre sur l’une de ces applications spécialisées de la modélisation du langage à partir de ressources limitées : l’extraction d’informations à partir de documents biomédicaux et cliniques en français. En particulier, nous montrons qu’en complétant le pré-entraînement en mots masqués des réseaux neuronaux transformer par des objectifs de prédiction extraits d’une base de connaissances biomédicales, leurs performances sur deux tâches différentes de reconnaissance d’entités nommées en français peuvent être augmentées.

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Entity Enhanced Attention Graph-Based Passages Retrieval
Lucas Albarede | Lorraine Goeuriot | Philippe Mulhem | Claude Le Pape-Gardeux | Sylvain Marie | Trinidad Chardin-Segui
Actes de CORIA-TALN 2023. Actes de la 18e Conférence en Recherche d'Information et Applications (CORIA)

Passage retrieval is crucial in specialized domains where documents are long and complex, such as patents, legal documents, scientific reports, etc. We explore in this paper the integration of Entities and passages in Heterogeneous Attention Graph Models dedicated to passage retrieval. We use the two passage retrieval architectures based on re-ranking proposed in [1]. We experiment our proposal on the TREC CAR Y3 Passage Retrieval Task. The results obtained show an improvement over state-of-the-art techniques and proves the effectiveness of the approach. Our experiments also show the importance of using adequate parameters for such approach.

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Vers l’évaluation continue des systèmes de recherche d’information.
Petra Galuscakova | Romain Deveaud | Gabriela Gonzalez-Saez | Philippe Mulhem | Lorraine Goeuriot | Florina Piroi | Martin Popel
Actes de CORIA-TALN 2023. Actes de la 18e Conférence en Recherche d'Information et Applications (CORIA)

Cet article présente le corpus de données associé à la première campagne évaluation LongEval dans le cadre de CLEF 2023. L’objectif de cette évaluation est d’étudier comment les systèmes de recherche d’informations réagissent à l’évolution des données qu’ils manipulent (notamment les documents et les requêtes). Nous détaillons les objectifs de la tâche, le processus d’acquisition des données et les mesures d’évaluation utilisées.

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UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition
Aidan Mannion | Didier Schwab | Lorraine Goeuriot
Proceedings of the 5th Clinical Natural Language Processing Workshop

Pre-trained transformer language models (LMs) have in recent years become the dominant paradigm in applied NLP. These models have achieved state-of-the-art performance on tasks such as information extraction, question answering, sentiment analysis, document classification and many others. In the biomedical domain, significant progress has been made in adapting this paradigm to NLP tasks that require the integration of domain-specific knowledge as well as statistical modelling of language. In particular, research in this area has focused on the question of how best to construct LMs that take into account not only the patterns of token distribution in medical text, but also the wealth of structured information contained in terminology resources such as the UMLS. This work contributes a data-centric paradigm for enriching the language representations of biomedical transformer-encoder LMs by extracting text sequences from the UMLS.This allows for graph-based learning objectives to be combined with masked-language pre-training. Preliminary results from experiments in the extension of pre-trained LMs as well as training from scratch show that this framework improves downstream performance on multiple biomedical and clinical Named Entity Recognition (NER) tasks. All pre-trained models, data processing pipelines and evaluation scripts will be made publicly available.

2021

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Identification de profil clinique du patient: Une approche de classification de séquences utilisant des modèles de langage français contextualisés (Identification of patient clinical profiles : A sequence classification approach using contextualised French language models )
Aidan Mannion | Thierry Chevalier | Didier Schwab | Lorraine Goeuriot
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Atelier DÉfi Fouille de Textes (DEFT)

Cet article présente un résumé de notre soumission pour Tâche 1 de DEFT 2021. Cette tâche consiste à identifier le profil clinique d’un patient à partir d’une description textuelle de son cas clinique en identifiant les types de pathologie mentionnés dans le texte. Ce travail étudie des approches de classification de texte utilisant des plongements de mots contextualisés en français. À partir d’une base de référence d’un modèle constitué pour la compréhension générale de la langue française, nous utilisons des modèles pré-entraînés avec masked language modelling et affinés à la tâche d’identification, en utilisant un corpus externe de textes cliniques fourni par SOS Médecins, pour développer des ensembles de classifieurs binaires associant les textes cliniques à des catégories de pathologies.

2018

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Building Evaluation Datasets for Cultural Microblog Retrieval
Lorraine Goeuriot | Josiane Mothe | Philippe Mulhem | Eric SanJuan
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Building Evaluation Datasets for Consumer-Oriented Information Retrieval
Lorraine Goeuriot | Liadh Kelly | Guido Zuccon | Joao Palotti
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Common people often experience difficulties in accessing relevant, correct, accurate and understandable health information online. Developing search techniques that aid these information needs is challenging. In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search solutions to support common people finding health information online. Specifically, the CLEF eHealth information retrieval (IR) task of this Lab has provided the research community with benchmarks for evaluating consumer-centered health information retrieval, thus fostering research and development aimed to address this challenging problem. Given consumer queries, the goal of the task is to retrieve relevant documents from the provided collection of web pages. The shared datasets provide a large health web crawl, queries representing people’s real world information needs, and relevance assessment judgements for the queries.

2014

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Porting a Summarizer to the French Language
Rémi Bois | Johannes Leveling | Lorraine Goeuriot | Gareth J. F. Jones | Liadh Kelly
Proceedings of TALN 2014 (Volume 2: Short Papers)

2009

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Compilation of Specialized Comparable Corpora in French and Japanese
Lorraine Goeuriot | Emmanuel Morin | Béatrice Daille
Proceedings of the 2nd Workshop on Building and Using Comparable Corpora: from Parallel to Non-parallel Corpora (BUCC)

2008

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Characterization of Scientific and Popular Science Discourse in French, Japanese and Russian
Lorraine Goeuriot | Natalia Grabar | Béatrice Daille
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We aim to characterize the comparability of corpora, we address this issue in the trilingual context through the distinction of expert and non expert documents. We work separately with corpora composed of documents from the medical domain in three languages (French, Japanese and Russian) which present an important linguistic distance between them. In our approach, documents are characterized in each language by their topic and by a discursive typology positioned at three levels of document analysis: structural, modal and lexical. The document typology is implemented with two learning algorithms (SVMlight and C4.5). Evaluation of results shows that the proposed discursive typology can be transposed from one language to another, as it indeed allows to distinguish the two aimed discourses (science and popular science). However, we observe that performances vary a lot according to languages, algorithms and types of discursive characteristics.

2007

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Caractérisation des discours scientifiques et vulgarisés en français, japonais et russe
Lorraine Goeuriot | Natalia Grabar | Béatrice Daille
Actes de la 14ème conférence sur le Traitement Automatique des Langues Naturelles. Posters

L’objectif principal de notre travail consiste à étudier la notion de comparabilité des corpus, et nous abordons cette question dans un contexte monolingue en cherchant à distinguer les documents scientifiques et vulgarisés. Nous travaillons séparément sur des corpus composés de documents du domaine médical dans trois langues à forte distance linguistique (le français, le japonais et le russe). Dans notre approche, les documents sont caractérisés dans chaque langue selon leur thématique et une typologie discursive qui se situe à trois niveaux de l’analyse des documents : structurel, modal et lexical. Le typage des documents est implémenté avec deux algorithmes d’apprentissage (SVMlight et C4.5). L’évaluation des résultats montre que la typologie discursive proposée est portable d’une langue à l’autre car elle permet en effet de distinguer les deux discours. Nous constatons néanmoins des performances très variées selon les langues, les algorithmes et les types de caractéristiques discursives.