4 citations · 11 across the 7 of their papers we have counts for
7 papers
OLISIA: a Cascade System for Spoken Dialogue State Tracking
Léo Jacqmin, Lucas Druart, Yannick Estève +3
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between…
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Mitja Nikolaus, Emmanuelle Salin, Stephane Ayache +2
Recent advances in vision-and-language modeling have seen the development of Transformer architectures that achieve remarkable performance on multimodal reasoning tasks. Yet, the e…
"Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking
Léo Jacqmin, Lina M. Rojas-Barahona, Benoit Favre
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 stat…
ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks
Valentin Pelloin, Franck Dary, Nicolas Herve +4
We aim at improving spoken language modeling (LM) using very large amount of automatically transcribed speech. We leverage the INA (French National Audiovisual Institute) collectio…
Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic
Simon Lupart, Benoit Favre, Vassilina Nikoulina +1
MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of publications in the biomedical domain. In the…
Robust Semantic Parsing with Adversarial Learning for Domain Generalization
Gabriel Marzinotto, Geraldine Damnati, Frédéric Béchet +1
This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for…
Building a robust sentiment lexicon with (almost) no resource
Mickael Rouvier, Benoit Favre
Creating sentiment polarity lexicons is labor intensive. Automatically translating them from resourceful languages requires in-domain machine translation systems, which rely on lar…