Context-Dependent SMT Model using Bilingual Verb-Noun Collocation

Proceedings of the 43rd Annual Meeting of the ACL, pages 549–556, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Context-dependent SMT Model using Bilingual Verb-Noun Collocation Young-Sook Hwang ATR SLT Research Labs 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto, 619-0288, JAPAN youngsook.hwang@atr.jp Yutaka Sasaki ATR SLT Research Labs 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto, 619-0288, JAPAN yutaka.sasaki@atr.jp Abstract In this paper, we propose a new context- dependent SMT model that is tightly cou- pled with a language model. It is de- signed to decrease the translation ambi- guities and efficiently search for an opti- mal hypothesis by reducing the hypothe- sis search space. It works through recipro- cal incorporation between source and tar- get context: a source word is determined by the context of previous and correspond- ing target words and the next target word is predicted by the pair consisting of the previous target word and its correspond- ing source word. In order to alleviate the data sparseness in chunk-based trans- lation, we take a stepwise back-off trans- lation strategy. Moreover, in order to ob- tain more semantically plausible transla- tion results, we use bilingual verb-noun collocations; these are automatically ex- tracted by using chunk alignment and a monolingual dependency parser. As a case study, we experimented on the language pair of Japanese and Korean. As a result, we could not only reduce the search space but also improve the performance. 1 Introduction For decades, many research efforts have contributed to the advance of statistical machine translation. Recently, various works have improved the quality of statistical machine translation systems by using phrase translation (Koehn et al., 2003; Marcu et al., 2002; Och et al., 1999; Och and Ney, 2000; Zens et al., 2004). Most of the phrase-based translation models have adopted the noisy-channel based IBM style models (Brown et al., 1993): � � � � � �� � ��� � � � � � �� � � �� � � �� � �� � � � (1) In these model, we have two types of knowledge: translation model, � � �� � � �� � � � and language model, � � �� � � �. The translation model links the source lan- guage sentence to the target language sentence. The language model describes the well-formedness of the target language sentence and might play a role in restricting hypothesis expansion during decoding. To recover the word order difference between two languages, it also allows modeling the reordering by introducing a relative distortion probability distribu- tion. However, in spite of using such a language model and a distortion model, the translation outputs may not be fluent or in fact may produce nonsense. To make things worse, the huge hypothesis search space is much too large for an exhaustive search. If arbitrary reorderings are allowed, the search prob- lem is NP-complete (Knight, 1999). According to a previous analysis (Koehn et al., 2004) of how many hypotheses are generated during an exhaustive search using the IBM models, the upper bound for the number of …

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