Example-based Machine Translation Based on Syntactic Transfer with Statistical Models

Example-based Machine Translation Based on Syntactic Transfer with Statistical Models Kenji Imamura, Hideo Okuma, Taro Watanabe, and Eiichiro Sumita ATR Spoken Language Translation Research Laboratories 2-2-2 Hikaridai, “Keihanna Science City” Kyoto, 619-0288, Japan {kenji.imamura,hideo.okuma,taro.watanabe,eiichiro.sumita}@atr.jp Abstract This paper presents example-based machine translation (MT) based on syntactic trans- fer, which selects the best translation by us- ing models of statistical machine translation. Example-based MT sometimes generates in- valid translations because it selects similar ex- amples to the input sentence based only on source language similarity. The method pro- posed in this paper selects the best transla- tion by using a language model and a trans- lation model in the same manner as statisti- cal MT, and it can improve MT quality over that of ‘pure’ example-based MT. A feature of this method is that the statistical models are applied after word re-ordering is achieved by syntactic transfer. This implies that MT quality is maintained even when we only ap- ply a lexicon model as the translation model. In addition, translation speed is improved by bottom-up generation, which utilizes the tree structure that is output from the syntactic transfer. 1 Introduction In response to the ongoing expansion of bilingual corpora, many machine translation (MT) meth- ods have been proposed that automatically ac- quire their knowledge or models from the cor- pora. Recently, two major approaches to such ma- chine translation have emerged: example-based machine translation and statistical machine trans- lation. Example-based MT (Nagao, 1984) regards a bilingual corpus as a database and retrieves exam- ples that are similar to an input sentence. Then, a translation is generated by modifying the tar- get part of the examples while referring to trans- lation dictionaries. Most example-based MT sys- tems employ phrases or sentences as the unit for examples, so they can translate while consider- ing case relations or idiomatic expressions. How- ever, when some examples conflict during re- E = J = A = NULL0 show1 me2 the3 one4 in5 the6 window7 uindo1 no2 shinamono3 o4 mise5 telidasai6 7 0 4 0 1 1 ( ) Figure 1: Example of Word Alignment between English and Japanese (Watanabe and Sumita, 2003) trieval, example-based MT selects the best exam- ple scored by the similarity between the input and the source part of the example. This implies that example-based MT does not check whether the translation of the given input sentence is correct or not. On the other hand, statistical MT employing IBM models (Brown et al., 1993) translates an in- put sentence by the combination of word transfer and word re-ordering. Therefore, when it is ap- plied to a language pair in which the word order is quite different (e.g., English and Japanese, Figure 1), it becomes difficult to find a globally optimal solution due to the enormous search space (Watan- abe and Sumita, 2003). Statistical MT could generate high-quality translations if it succeeded in finding a globally optimal solution. Therefore, the models employed by statistical MT are superior indicators of the quality of machine translation. Using …

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