Ordering Phrases with Function Words

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 712–719, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Ordering Phrases with Function Words Hendra Setiawan and Min-Yen Kan School of Computing National University of Singapore Singapore 117543 {hendrase,kanmy}@comp.nus.edu.sg Haizhou Li Institute for Infocomm Research 21 Heng Mui Keng Terrace Singapore 119613 hli@i2r.a-star.edu.sg Abstract This paper presents a Function Word cen- tered, Syntax-based (FWS) solution to ad- dress phrase ordering in the context of statistical machine translation (SMT). Mo- tivated by the observation that function words often encode grammatical relation- ship among phrases within a sentence, we propose a probabilistic synchronous gram- mar to model the ordering of function words and their left and right arguments. We im- prove phrase ordering performance by lexi- calizing the resulting rules in a small number of cases corresponding to function words. The experiments show that the FWS ap- proach consistently outperforms the base- line system in ordering function words’ ar- guments and improving translation quality in both perfect and noisy word alignment scenarios. 1 Introduction The focus of this paper is on function words, a class of words with little intrinsic meaning but is vital in expressing grammatical relationships among words within a sentence. Such encoded grammatical infor- mation, often implicit, makes function words piv- otal in modeling structural divergences, as project- ing them in different languages often result in long- range structural changes to the realized sentences. Just as a foreign language learner often makes mistakes in using function words, we observe that current machine translation (MT) systems often per- form poorly in ordering function words’ arguments; lexically correct translations often end up reordered incorrectly. Thus, we are interested in modeling the structural divergence encoded by such function words. A key finding of our work is that modeling the ordering of the dependent arguments of function words results in better translation quality. Most current systems use statistical knowledge obtained from corpora in favor of rich natural lan- guage knowledge. Instead of using syntactic knowl- edge to determine function words, we approximate this by equating the most frequent words as func- tion words. By explicitly modeling phrase ordering around these frequent words, we aim to capture the most important and prevalent ordering productions. 2 Related Work A good translation should be both faithful with ade- quate lexical choice to the source language and flu- ent in its word ordering to the target language. In pursuit of better translation, phrase-based models (Och and Ney, 2004) have significantly improved the quality over classical word-based models (Brown et al., 1993). These multiword phrasal units contribute to fluency by inherently capturing intra-phrase re- ordering. However, despite this progress, inter- phrase reordering (especially long distance ones) still poses a great challenge to statistical machine translation (SMT). The basic phrase reordering model is a simple unlexicalized, context-insensitive distortion penalty model (Koehn et al., 2003). This model assumes little or no structural divergence between language pairs, preferring the original, translated order by pe- nalizing reordering. …

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