Léo Jacqmin


2022

pdf bib
« Est-ce que tu me suis ? » : une revue du suivi de l’état du dialogue (“Do you follow me ?" : a review of dialogue state tracking )
Léo Jacqmin
Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 : 24e Rencontres Etudiants Chercheurs en Informatique pour le TAL (RECITAL)

Tout en communiquant avec un utilisateur, un système de dialogue orienté tâche doit suivre les besoins de l’utilisateur à chaque étape selon l’historique de la conversation. Ce procédé appelé suivi de l’état du dialogue est primordial car il informe directement les actions du système. Cet article présente dans un premier temps la tâche du suivi de l’état du dialogue, les jeux de données disponibles et les approches modernes. Ensuite, compte tenu du nombre important de publications des dernières années, il vise à recenser les point saillants et les avancées des recherches. Bien que les approches neuronales aient permis des progrès notables, nous argumentons que certains aspects critiques liés aux systèmes de dialogue sont encore trop peu explorés. Pour motiver de futures études, plusieurs pistes de recherche sont proposées.

pdf bib
“Do you follow me?”: A Survey of Recent Approaches in Dialogue State Tracking
Léo Jacqmin | Lina M. Rojas Barahona | Benoit Favre
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

While communicating with a user, a task-oriented dialogue system has to track the user’s needs at each turn according to the conversation history. This process called dialogue state tracking (DST) is crucial because it directly informs the downstream dialogue policy. DST has received a lot of interest in recent years with the text-to-text paradigm emerging as the favored approach. In this review paper, we first present the task and its associated datasets. Then, considering a large number of recent publications, we identify highlights and advances of research in 2021-2022. Although neural approaches have enabled significant progress, we argue that some critical aspects of dialogue systems such as generalizability are still underexplored. To motivate future studies, we propose several research avenues.

2021

pdf bib
GECko+: a Grammatical and Discourse Error Correction Tool
Eduardo Calò | Léo Jacqmin | Thibo Rosemplatt | Maxime Amblard | Miguel Couceiro | Ajinkya Kulkarni
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 : Démonstrations

GECko+ : a Grammatical and Discourse Error Correction Tool We introduce GECko+, a web-based writing assistance tool for English that corrects errors both at the sentence and at the discourse level. It is based on two state-of-the-art models for grammar error correction and sentence ordering. GECko+ is available online as a web application that implements a pipeline combining the two models.

pdf bib
SpanAlign: Efficient Sequence Tagging Annotation Projection into Translated Data applied to Cross-Lingual Opinion Mining
Léo Jacqmin | Gabriel Marzinotto | Justyna Gromada | Ewelina Szczekocka | Robert Kołodyński | Géraldine Damnati
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

Following the increasing performance of neural machine translation systems, the paradigm of using automatically translated data for cross-lingual adaptation is now studied in several applicative domains. The capacity to accurately project annotations remains however an issue for sequence tagging tasks where annotation must be projected with correct spans. Additionally, when the task implies noisy user-generated text, the quality of translation and annotation projection can be affected. In this paper we propose to tackle multilingual sequence tagging with a new span alignment method and apply it to opinion target extraction from customer reviews. We show that provided suitable heuristics, translated data with automatic span-level annotation projection can yield improvements both for cross-lingual adaptation compared to zero-shot transfer, and data augmentation compared to a multilingual baseline.