Towards Accurate Predictors of Word Quality for Machine Translation: Lessons Learned on French - English and English - Spanish Systems

Ngoc-Quang Luong (1) , Laurent Besacier (2, 3, 1) , Benjamin Lecouteux (1, 2)

1 LIG - Laboratoire d'Informatique de Grenoble
2 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole
3 IUF - Institut universitaire de France

This paper proposes some ideas to build effective estimators, which predict the quality of words in a Machine Translation (MT) output. We propose a number of novel features of various types (system-based, lexical, syntactic and semantic) and then integrate them into the conventional (previously used) feature set, for our baseline classifier training. The classifiers are built over two different bilingual corpora: French–English (fr–en) and English–Spanish (en–es). After the experiments with all features, we deploy a " Feature Selection " strategy to filter the best performing ones. Then, a method that combines multiple " weak " classifiers to constitute a strong " composite " classifier by taking advantage of their complementarity allows us to achieve a significant improvement in terms of F-score, for both fr–en and en–es systems. Finally, we exploit word confidence scores for improving the quality estimation system at sentence level.

Mots clés: quality estimation; word confidence estimation; statistical machine translation

Domaines: Informatique et langage [cs.CL]

Ngoc-Quang Luong, Laurent Besacier, Benjamin Lecouteux. Towards Accurate Predictors of Word Quality for Machine Translation: Lessons Learned on French - English and English - Spanish Systems. Data and Knowledge Engineering, 2015, pp.11. ⟨10.1016/j.datak.2015.04.003⟩. ⟨hal-01147902⟩