Improving Statistical Machine Translation with Monolingual Collocation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 825–833, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Improving Statistical Machine Translation with Monolingual Collocation Zhanyi Liu1, Haifeng Wang2, Hua Wu2, Sheng Li1 1Harbin Institute of Technology, Harbin, China 2Baidu.com Inc., Beijing, China zhanyiliu@gmail.com {wanghaifeng, wu_hua}@baidu.com lisheng@hit.edu.cn Abstract This paper proposes to use monolingual collocations to improve Statistical Ma- chine Translation (SMT). We make use of the collocation probabilities, which are estimated from monolingual corpora, in two aspects, namely improving word alignment for various kinds of SMT sys- tems and improving phrase table for phrase-based SMT. The experimental re- sults show that our method improves the performance of both word alignment and translation quality significantly. As com- pared to baseline systems, we achieve ab- solute improvements of 2.40 BLEU score on a phrase-based SMT system and 1.76 BLEU score on a parsing-based SMT system. 1 Introduction Statistical bilingual word alignment (Brown et al. 1993) is the base of most SMT systems. As com- pared to single-word alignment, multi-word alignment is more difficult to be identified. Al- though many methods were proposed to improve the quality of word alignments (Wu, 1997; Och and Ney, 2000; Marcu and Wong, 2002; Cherry and Lin, 2003; Liu et al., 2005; Huang, 2009), the correlation of the words in multi-word alignments is not fully considered. In phrase-based SMT (Koehn et al., 2003), the phrase boundary is usually determined based on the bi-directional word alignments. But as far as we know, few previous studies exploit the collo- cation relations of the words in a phrase. Some This work was partially done at Toshiba (China) Research and Development Center. researches used soft syntactic constraints to pre- dict whether source phrase can be translated to- gether (Marton and Resnik, 2008; Xiong et al., 2009). However, the constraints were learned from the parsed corpus, which is not available for many languages. In this paper, we propose to use monolingual collocations to improve SMT. We first identify potentially collocated words and estimate collo- cation probabilities from monolingual corpora using a Monolingual Word Alignment (MWA) method (Liu et al., 2009), which does not need any additional resource or linguistic preprocess- ing, and which outperforms previous methods on the same experimental data. Then the collocation information is employed to improve Bilingual Word Alignment (BWA) for various kinds of SMT systems and to improve phrase table for phrase-based SMT. To improve BWA, we re-estimate the align- ment probabilities by using the collocation prob- abilities of words in the same cept. A cept is the set of source words that are connected to the same target word (Brown et al., 1993). An alignment between a source multi-word cept and a target word is a many-to-one multi-word alignment. To improve phrase table, we calculate phrase collocation probabilities based on word colloca- tion probabilities. Then the phrase collocation probabilities are used as additional features in phrase-based SMT systems. The evaluation results show that the proposed method in this paper significantly …

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