SPMT: Statistical Machine Translation with Syntactified Target Language Phrases

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 44–52, Sydney, July 2006. c⃝2006 Association for Computational Linguistics SPMT: Statistical Machine Translation with Syntactified Target Language Phrases Daniel Marcu, Wei Wang, Abdessamad Echihabi, and Kevin Knight Language Weaver Inc. 4640 Admiralty Way, Suite 1210 Marina del Rey, CA 90292 {dmarcu,wwang,aechihabi,kknight}@languageweaver.com Abstract We introduce SPMT, a new class of sta- tistical Translation Models that use Syn- tactified target language Phrases. The SPMT models outperform a state of the art phrase-based baseline model by 2.64 Bleu points on the NIST 2003 Chinese-English test corpus and 0.28 points on a human- based quality metric that ranks translations on a scale from 1 to 5. 1 Introduction During the last four years, various implemen- tations and extentions to phrase-based statistical models (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004) have led to signif- icant increases in machine translation accuracy. Although phrase-based models yield high-quality translations for language pairs that exhibit simi- lar word order, they fail to produce grammatical outputs for language pairs that are syntactically divergent. Recent models that exploit syntactic information of the source language (Quirk et al., 2005) have been shown to produce better outputs than phrase-based systems when evaluated on rel- atively small scale, domain specific corpora. And syntax-inspired formal models (Chiang, 2005), in spite of being trained on significantly less data, have shown promising results when compared on the same test sets with mature phrase-based sys- tems. To our knowledge though, no previous re- search has demonstrated that a syntax-based sta- tistical translation system could produce better re- sults than a phrase-based system on a large-scale, well-established, open domain translation task. In this paper we present such a system. Our translation models rely upon and naturally exploit submodels (feature functions) that have been initially developed in phrase-based systems for choosing target translations of source language phrases, and use new, syntax-based translation and target language submodels for assembling target phrases into well-formed, grammatical outputs. After we introduce our models intuitively, we discuss their formal underpinning and parameter training in Section 2. In Section 3, we present our decoder and, in Section 4, we evaluate our models empirically. In Section 5, we conclude with a brief discussion. 2 SPMT: statistical Machine Translation with Syntactified Phrases 2.1 An intuitive introduction to SPMT After being exposed to 100M+ words of parallel Chinese-English texts, current phrase-based statis- tical machine translation learners induce reason- ably reliable phrase-based probabilistic dictionar- ies. For example, our baseline statistical phrase- based system learns that, with high probabilities, the Chinese phrases “ASTRO- -NAUTS”, “FRANCE AND RUSSIA” and “COMINGFROM” can be trans- lated into English as “astronauts”/“cosmonauts”, “france and russia”/“france and russian” and “coming from”/“from”, respectively. 1 Unfortu- nately, when given as input Chinese sentence 1, our phrase-based system produces the output shown in 2 and not the translation in 3, which correctly orders the phrasal translations into a grammatical sequence. We believe this hap- pens because the distortion/reordering models that …

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