Camille Pradel


2020

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Load What You Need: Smaller Versions of Mutililingual BERT
Amine Abdaoui | Camille Pradel | Grégoire Sigel
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

Pre-trained Transformer-based models are achieving state-of-the-art results on a variety of Natural Language Processing data sets. However, the size of these models is often a drawback for their deployment in real production applications. In the case of multilingual models, most of the parameters are located in the embeddings layer. Therefore, reducing the vocabulary size should have an important impact on the total number of parameters. In this paper, we propose to extract smaller models that handle fewer number of languages according to the targeted corpora. We present an evaluation of smaller versions of multilingual BERT on the XNLI data set, but we believe that this method may be applied to other multilingual transformers. 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. We compared our models with DistilmBERT (a distilled version of multilingual BERT) and showed that unlike language reduction, distillation induced a 1.7% to 6% drop in the overall accuracy on the XNLI data set. The presented models and code are publicly available.

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DiscSense: Automated Semantic Analysis of Discourse Markers
Damien Sileo | Tim Van de Cruys | Camille Pradel | Philippe Muller
Proceedings of the 12th Language Resources and Evaluation Conference

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). These predictions allow us to study the link between discourse markers and the semantic relations annotated in classification datasets. Handcrafted mappings have been proposed between markers and discourse relations on a limited set of markers and a limited set of categories, but there exists hundreds of discourse markers expressing a wide variety of relations, and there is no consensus on the taxonomy of relations between competing discourse theories (which are largely built in a top-down fashion). By using an automatic prediction method over existing semantically annotated datasets, we provide a bottom-up characterization of discourse markers in English. The resulting dataset, named DiscSense, is publicly available.

2019

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Composition of Sentence Embeddings: Lessons from Statistical Relational Learning
Damien Sileo | Tim Van De Cruys | Camille Pradel | Philippe Muller
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Various NLP problems – such as the prediction of sentence similarity, entailment, and discourse relations – are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. concatenation or sum) of those vectors as features for the prediction. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.

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Aprentissage non-supervisé pour l’appariement et l’étiquetage de cas cliniques en français - DEFT2019 (Unsupervised learning for matching and labelling of french clincal cases - DEFT2019 )
Damien Sileo | Tim Van de Cruys | Philippe Muller | Camille Pradel
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Défi Fouille de Textes (atelier TALN-RECITAL)

Nous présentons le système utilisé par l’équipe Synapse/IRIT dans la compétition DEFT2019 portant sur deux tâches liées à des cas cliniques rédigés en français : l’une d’appariement entre des cas cliniques et des discussions, l’autre d’extraction de mots-clefs. Une des particularité est l’emploi d’apprentissage non-supervisé sur les deux tâches, sur un corpus construit spécifiquement pour le domaine médical en français

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Mining Discourse Markers for Unsupervised Sentence Representation Learning
Damien Sileo | Tim Van De Cruys | Camille Pradel | Philippe Muller
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data – such as discourse markers between sentences – mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as “coincidentally” or “amazingly”. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it’s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.

2018

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Concaténation de réseaux de neurones pour la classification de tweets, DEFT2018 (Concatenation of neural networks for tweets classification, DEFT2018 )
Damien Sileo | Tim Van de Cruys | Philippe Muller | Camille Pradel
Actes de la Conférence TALN. Volume 2 - Démonstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT

Nous présentons le système utilisé par l’équipe Melodi/Synapse Développement dans la compétition DEFT2018 portant sur la classification de thématique ou de sentiments de tweets en français. On propose un système unique pour les deux approches qui combine concaténativement deux méthodes d’embedding et trois modèles de représentation séquence. Le système se classe 1/13 en analyse de sentiments et 4/13 en classification thématique.

2017

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Changement stylistique de phrases par apprentissage faiblement supervisé (Textual Style Transfer using Weakly Supervised Learning)
Damien Sileo | Camille Pradel | Philippe Muller | Tim Van de Cruys
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

Plusieurs tâches 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 données et modèles. Nous introduisons ici une méthode générale s’adressant à tous ces problèmes, utilisant des données plus simples à obtenir : un ensemble de phrases munies d’indicateurs sur leur style, comme des phrases et le type de sentiment qu’elles expriment. Cette méthode repose sur un modèle d’apprentissage de représentations non supervisé (un auto-encodeur variationnel), puis sur le changement des représentations apprises pour correspondre à un style donné. Le résultat est évalué qualitativement, puis quantitativement sur le jeu de données de compression de phrases Microsoft, avec des résultats encourageants.