Statistical Analysis of Alignment Characteristics for Phrase-based Machine Translation

Patrik Lambert (1) , Simon Petitrenaud (1) , Yanjun Ma (2, 3) , Andy Way (2, 3)

1 LIUM - Laboratoire d'Informatique de l'Université du Mans
2 NCLT - National Centre for Language Technology
3 CNGL - Centre for Next Generation Localisation

In most statistical machine translation (SMT) systems, bilingual segments are extracted via word alignment. However, there lacks systematic study as to what alignment characteristics can benefit MT under specific experimental settings such as the language pair or the corpus size. In this paper we produce a set of alignments by directly tuning the alignment model according to alignment F-score and BLEU score in order to investigate the alignment characteristics that are helpful in translation. We report results for a phrase-based SMT system on Chinese-to-English IWSLT data, and Spanish-to-English European Parliament data. With a statistical analysis into alignment characteristics that are correlated with BLEU score, we give alignment hints to improve BLEU score using a phrase-based SMT system and different types of corpus.

Mots clés: machine translation; word alignment; discriminative training; statistical analysis

Domaines: Traitement du texte et du document

Patrik Lambert, Simon Petitrenaud, Yanjun Ma, Andy Way. Statistical Analysis of Alignment Characteristics for Phrase-based Machine Translation. Proceedings of the 14th European Association for Machine Translation, May 2010, Saint-Raphaël, France. no page number. ⟨hal-00525181⟩