Effective Use of Function Words for Rule Generalization in Forest-Based Translation

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 22–31, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Effective Use of Function Words for Rule Generalization in Forest-Based Translation Xianchao Wu† Takuya Matsuzaki† Jun’ichi Tsujii†‡∗ †Department of Computer Science, The University of Tokyo ‡School of Computer Science, University of Manchester ∗National Centre for Text Mining (NaCTeM) {wxc, matuzaki, tsujii}@is.s.u-tokyo.ac.jp Abstract In the present paper, we propose the ef- fective usage of function words to generate generalized translation rules for forest-based translation. Given aligned forest-string pairs, we extract composed tree-to-string translation rules that account for multiple interpretations of both aligned and unaligned target func- tion words. In order to constrain the ex- haustive attachments of function words, we limit to bind them to the nearby syntactic chunks yielded by a target dependency parser. Therefore, the proposed approach can not only capture source-tree-to-target-chunk cor- respondences but can also use forest structures that compactly encode an exponential num- ber of parse trees to properly generate target function words during decoding. Extensive experiments involving large-scale English-to- Japanese translation revealed a significant im- provement of 1.8 points in BLEU score, as compared with a strong forest-to-string base- line system. 1 Introduction Rule generalization remains a key challenge for current syntax-based statistical machine translation (SMT) systems. On the one hand, there is a ten- dency to integrate richer syntactic information into a translation rule in order to better express the trans- lation phenomena. Thus, flat phrases (Koehn et al., 2003), hierarchical phrases (Chiang, 2005), and syn- tactic tree fragments (Galley et al., 2006; Mi and Huang, 2008; Wu et al., 2010) are gradually used in SMT. On the other hand, the use of syntactic phrases continues due to the requirement for phrase cover- age in most syntax-based systems. For example, Mi et al. (2008) achieved a 3.1-point improvement in BLEU score (Papineni et al., 2002) by including bilingual syntactic phrases in their forest-based sys- tem. Compared with flat phrases, syntactic rules are good at capturing global reordering, which has been reported to be essential for translating between lan- guages with substantial structural differences, such as English and Japanese, which is a subject-object- verb language (Xu et al., 2009). Forest-based translation frameworks, which make use of packed parse forests on the source and/or tar- get language side(s), are an increasingly promising approach to syntax-based SMT, being both algorith- mically appealing (Mi et al., 2008) and empirically successful (Mi and Huang, 2008; Liu et al., 2009). However, forest-based translation systems, and, in general, most linguistically syntax-based SMT sys- tems (Galley et al., 2004; Galley et al., 2006; Liu et al., 2006; Zhang et al., 2007; Mi et al., 2008; Liu et al., 2009; Chiang, 2010), are built upon word aligned parallel sentences and thus share a critical dependence on word alignments. For example, even a single spurious word alignment can invalidate a large number of otherwise extractable rules, and un- aligned words can result in an exponentially large set of extractable rules …

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