Japanese Argument Reordering Based on Dependency Structure for Statistical Machine Translation

Chooi-Ling GOH  Taro WATANABE  Eiichiro SUMITA  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.6   pp.1668-1675
Publication Date: 2012/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1668
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Natural Language Processing
predicate-argument structure,  reordering,  paraphrasing,  re-ranking,  statistical machine translation,  

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While phrase-based statistical machine translation systems prefer to translate with longer phrases, this may cause errors in a free word order language, such as Japanese, in which the order of the arguments of the predicates is not solely determined by the predicates and the arguments can be placed quite freely in the text. In this paper, we propose to reorder the arguments but not the predicates in Japanese using a dependency structure as a kind of reordering. Instead of a single deterministically given permutation, we generate multiple reordered phrases for each sentence and translate them independently. Then we apply a re-ranking method using a discriminative approach by Ranking Support Vector Machines (SVM) to re-score the multiple reordered phrase translations. In our experiment with the travel domain corpus BTEC, we gain a 1.22% BLEU score improvement when only 1-best is used for re-ranking and 4.12% BLEU score improvement when n-best is used for Japanese-English translation.