Nicolas Pécheux


2015

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LIMSI@WMT’15 : Translation Task
Benjamin Marie | Alexandre Allauzen | Franck Burlot | Quoc-Khanh Do | Julia Ive | Elena Knyazeva | Matthieu Labeau | Thomas Lavergne | Kevin Löser | Nicolas Pécheux | François Yvon
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Why Predicting Post-Edition is so Hard? Failure Analysis of LIMSI Submission to the APE Shared Task
Guillaume Wisniewski | Nicolas Pécheux | François Yvon
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Oublier ce qu’on sait, pour mieux apprendre ce qu’on ne sait pas : une étude sur les contraintes de type dans les modèles CRF
Nicolas Pécheux | Alexandre Allauzen | Thomas Lavergne | Guillaume Wisniewski | François Yvon
Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Quand on dispose de connaissances a priori sur les sorties possibles d’un problème d’étiquetage, il semble souhaitable d’inclure cette information lors de l’apprentissage pour simplifier la tâche de modélisation et accélérer les traitements. Pourtant, même lorsque ces contraintes sont correctes et utiles au décodage, leur utilisation lors de l’apprentissage peut dégrader sévèrement les performances. Dans cet article, nous étudions ce paradoxe et montrons que le manque de contraste induit par les connaissances entraîne une forme de sous-apprentissage qu’il est cependant possible de limiter.

2014

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Cross-Lingual Part-of-Speech Tagging through Ambiguous Learning
Guillaume Wisniewski | Nicolas Pécheux | Souhir Gahbiche-Braham | François Yvon
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Rule-based Reordering Space in Statistical Machine Translation
Nicolas Pécheux | Alexander Allauzen | François Yvon
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In Statistical Machine Translation (SMT), the constraints on word reorderings have a great impact on the set of potential translations that are explored. Notwithstanding computationnal issues, the reordering space of a SMT system needs to be designed with great care: if a larger search space is likely to yield better translations, it may also lead to more decoding errors, because of the added ambiguity and the interaction with the pruning strategy. In this paper, we study this trade-off using a state-of-the art translation system, where all reorderings are represented in a word lattice prior to decoding. This allows us to directly explore and compare different reordering spaces. We study in detail a rule-based preordering system, varying the length or number of rules, the tagset used, as well as contrasting with oracle settings and purely combinatorial subsets of permutations. We focus on two language pairs: English-French, a close language pair and English-German, known to be a more challenging reordering pair.

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LIMSI @ WMT’14 Medical Translation Task
Nicolas Pécheux | Li Gong | Quoc Khanh Do | Benjamin Marie | Yulia Ivanishcheva | Alexander Allauzen | Thomas Lavergne | Jan Niehues | Aurélien Max | François Yvon
Proceedings of the Ninth Workshop on Statistical Machine Translation

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LIMSI Submission for WMT’14 QE Task
Guillaume Wisniewski | Nicolas Pécheux | Alexander Allauzen | François Yvon
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Cross-Lingual POS Tagging through Ambiguous Learning: First Experiments (Apprentissage partiellement supervisé d’un étiqueteur morpho-syntaxique par transfert cross-lingue) [in French]
Guillaume Wisniewski | Nicolas Pécheux | Elena Knyazeva | Alexandre Allauzen | François Yvon
Proceedings of TALN 2014 (Volume 1: Long Papers)

2013

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LIMSI @ WMT13
Alexander Allauzen | Nicolas Pécheux | Quoc Khanh Do | Marco Dinarelli | Thomas Lavergne | Aurélien Max | Hai-Son Le | François Yvon
Proceedings of the Eighth Workshop on Statistical Machine Translation