Phrasal Cohesion and Statistical Machine Translation

Phrasal Cohesion and Statistical Machine Translation Heidi J. Fox Brown Laboratory for Linguistic Information Processing Department of Computer Science Brown University, Box 1910, Providence, RI 02912 hjf@cs.brown.edu Abstract There has been much interest in us- ing phrasal movement to improve statis- tical machine translation. We explore how well phrases cohere across two lan- guages, specifically English and French, and examine the particular conditions un- der which they do not. We demonstrate that while there are cases where coherence is poor, there are many regularities which can be exploited by a statistical machine translation system. We also compare three variant syntactic representations to deter- mine which one has the best properties with respect to cohesion. 1 Introduction Statistical machine translation (SMT) seeks to de- velop mathematical models of the translation pro- cess whose parameters can be automatically esti- mated from a parallel corpus. The first work in SMT, done at IBM (Brown et al., 1993), developed a noisy-channel model, factoring the translation pro- cess into two portions: the translation model and the language model. The translation model captures the translation of source language words into the target language and the reordering of those words. The language model ranks the outputs of the translation model by how well they adhere to the syntactic con- straints of the target language.1 The prime deficiency of the IBM model is the re- ordering component. Even in the most complex of 1Though usually a simple word n-gram model is used for the language model. the five IBM models, the reordering operation pays little attention to context and none at all to higher- level syntactic structures. Many attempts have been made to remedy this by incorporating syntactic in- formation into translation models. These have taken several different forms, but all share the basic as- sumption that phrases in one language tend to stay together (i.e. cohere) during translation and thus the word-reordering operation can move entire phrases, rather than moving each word independently. (Yarowsky et al., 2001) states that during their work on noun phrase bracketing they found a strong cohesion among noun phrases, even when compar- ing English to Czech, a relatively free word or- der language. Other than this, there is little in the SMT literature to validate the coherence assump- tion. Several studies have reported alignment or translation performance for syntactically augmented translation models (Wu, 1997; Wang, 1998; Alshawi et al., 2000; Yamada and Knight, 2001; Jones and Havrilla, 1998) and these results have been promis- ing. However, without a focused study of the be- havior of phrases across languages, we cannot know how far these models can take us and what specific pitfalls they face. The particulars of cohesion will clearly depend upon the pair of languages being compared. Intu- itively, we expect that while French and Spanish will have a high degree of cohesion, French and Japanese may not. It is also clear that if the cohesion between two closely related languages is not high enough to be useful, then there is …

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