A Syntax-based Statistical Translation Model

A Syntax-based Statistical Translation Model Kenji Yamada and Kevin Knight Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 � kyamada,knight � @isi.edu Abstract We present a syntax-based statistical translation model. Our model trans- forms a source-language parse tree into a target-language string by apply- ing stochastic operations at each node. These operations capture linguistic dif- ferences such as word order and case marking. Model parameters are esti- mated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5. 1 Introduction A statistical translation model (TM) is a mathe- matical model in which the process of human- language translation is statistically modeled. Model parameters are automatically estimated us- ing a corpus of translation pairs. TMs have been used for statistical machine translation (Berger et al., 1996), word alignment of a translation cor- pus (Melamed, 2000), multilingual document re- trieval (Franz et al., 1999), automatic dictionary construction (Resnik and Melamed, 1997), and data preparation for word sense disambiguation programs (Brown et al., 1991). Developing a bet- ter TM is a fundamental issue for those applica- tions. Researchers at IBM first described such a sta- tistical TM in (Brown et al., 1988). Their mod- els are based on a string-to-string noisy channel model. The channel converts a sequence of words in one language (such as English) into another (such as French). The channel operations are movements, duplications, and translations, ap- plied to each word independently. The movement is conditioned only on word classes and positions in the string, and the duplication and translation are conditioned only on the word identity. Math- ematical details are fully described in (Brown et al., 1993). One criticism of the IBM-style TM is that it does not model structural or syntactic aspects of the language. The TM was only demonstrated for a structurally similar language pair (English and French). It has been suspected that a language pair with very different word order such as En- glish and Japanese would not be modeled well by these TMs. To incorporate structural aspects of the lan- guage, our channel model accepts a parse tree as an input, i.e., the input sentence is preprocessed by a syntactic parser. The channel performs oper- ations on each node of the parse tree. The oper- ations are reordering child nodes, inserting extra words at each node, and translating leaf words. Figure 1 shows the overview of the operations of our model. Note that the output of our model is a string, not a parse tree. Therefore, parsing is only needed on the channel input side. The reorder operation is intended to model translation between languages with different word orders, such as SVO-languages (English or Chi- nese) and SOV-languages (Japanese or Turkish). The word-insertion operation is intended to cap- ture linguistic differences in specifying syntactic cases. E.g., English and French use structural po- sition to specify case, while Japanese and Korean use case-marker particles. Wang …

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