LIG System for WMT13 QE Task: Investigating the Usefulness of Features in Word Confidence Estimation for MT

Abstract : This paper presents the LIG's systems submitted for Task 2 of WMT13 Quality Estimation campaign. This is a word confidence estimation (WCE) task where each participant was asked to label each word in a translated text as a binary (Keep/Change) or multi-class (Keep/Substitute/Delete) category. We integrate a number of features of various types (system-based, lexical, syntactic and semantic) into the conventional feature set, for our baseline classifier training. After the experiments with all features, we deploy a " Feature Selection " strategy to keep only the best performing ones. Then, a method that combines multiple " weak " classifiers to build a strong " composite " classifier by taking advantage of their complementarity is presented and experimented. We then select the best systems for submission and present the official results obtained.
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Communication dans un congrès
8th Workshop on Statistical Machine Translation, 2013, Sofia, Bulgaria. Proceedings of the 8th Workshop on Statistical Machine Translation, pp.386-391, 2013
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Soumis le : jeudi 23 novembre 2017 - 11:51:06
Dernière modification le : jeudi 11 octobre 2018 - 08:48:03

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Ngoc-Quang Luong, Benjamin Lecouteux, Laurent Besacier. LIG System for WMT13 QE Task: Investigating the Usefulness of Features in Word Confidence Estimation for MT. 8th Workshop on Statistical Machine Translation, 2013, Sofia, Bulgaria. Proceedings of the 8th Workshop on Statistical Machine Translation, pp.386-391, 2013. 〈hal-00953773〉

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