1 code implementation • EMNLP (sustainlp) 2020 • Amine Abdaoui, Camille Pradel, Grégoire Sigel
The obtained results confirm that we can generate smaller models that keep comparable results, while reducing up to 45% of the total number of parameters.
no code implementations • ICLR 2019 • Damien Sileo, Tim Van De Cruys, Camille Pradel, Philippe Muller
In this work, we show that textual relational models implicitly use compositions from baseline SRL models.
no code implementations • LREC 2022 • Hadjer Khaldi, Farah Benamara, Camille Pradel, Grégoire Sigel, Nathalie Aussenac-Gilles
The business world has changed due to the 21st century economy, where borders have melted and trades became free.
2 code implementations • 12 Oct 2020 • Amine Abdaoui, Camille Pradel, Grégoire Sigel
The obtained results confirm that we can generate smaller models that keep comparable results, while reducing up to 45% of the total number of parameters.
1 code implementation • LREC 2020 • Damien Sileo, Tim Van De Cruys, Camille Pradel, Philippe Muller
In this work, we take another perspective: using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets).
2 code implementations • LREC 2022 • Damien Sileo, Tim Van-De-Cruys, Camille Pradel, Philippe Muller
New models for natural language understanding have recently made an unparalleled amount of progress, which has led some researchers to suggest that the models induce universal text representations.
no code implementations • JEPTALNRECITAL 2019 • Damien Sileo, Tim Van De Cruys, Philippe Muller, Camille Pradel
Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l{'}{\'e}quipe Synapse/IRIT dans la comp{\'e}tition DEFT2019 portant sur deux t{\^a}ches li{\'e}es {\`a} des cas cliniques r{\'e}dig{\'e}s en fran{\c{c}}ais : l{'}une d{'}appariement entre des cas cliniques et des discussions, l{'}autre d{'}extraction de mots-clefs.
no code implementations • SEMEVAL 2019 • Damien Sileo, Tim Van De Cruys, Camille Pradel, Philippe Muller
In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models.
no code implementations • 4 Apr 2019 • Damien Sileo, Tim Van-De-Cruys, Camille Pradel, Philippe Muller
In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models.
1 code implementation • NAACL 2019 • Damien Sileo, Tim Van-De-Cruys, Camille Pradel, Philippe Muller
Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct.
Ranked #1 on Relation Classification on Discovery
no code implementations • JEPTALNRECITAL 2018 • Damien Sileo, Tim Van De Cruys, Philippe Muller, Camille Pradel
Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l{'}{\'e}quipe Melodi/Synapse D{\'e}veloppement dans la comp{\'e}tition DEFT2018 portant sur la classification de th{\'e}matique ou de sentiments de tweets en fran{\c{c}}ais.
no code implementations • 14 Sep 2017 • Damien Sileo, Camille Pradel, Philippe Muller, Tim Van De Cruys
We present our system for the CAp 2017 NER challenge which is about named entity recognition on French tweets.
no code implementations • JEPTALNRECITAL 2017 • Damien Sileo, Camille Pradel, Philippe Muller, Tim Van De Cruys
Plusieurs t{\^a}ches en traitement du langage naturel impliquent de modifier des phrases en conservant au mieux leur sens, comme la reformulation, la compression, la simplification, chacune avec leurs propres donn{\'e}es et mod{\`e}les.