RI: Small: Statistical Machine Translation Through a Tree Adjoining Grammar with Flexible Parsing Operations

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Our research involves the development of a syntactic approach forstatistical machine translation that extends a tree adjoining grammar(TAG) formalism to the translation problem, and frames translationdirectly as a parsing problem. The model imposes no constraints onentries in the phrasal lexicon, thereby retaining the flexible lexicalentries of phrase-based translation systems; it allows straightforwardincorporation of a syntactic language model. The operations used tocombine tree fragments into a complete parse tree are generalizationsof standard parsing operations found in TAG; specifically, they aremodified to be highly flexible, potentially allowing any possiblepermutation (reordering) of the initial fragments. This allows themodel a great deal of freedom in capturing differences in word orderbetween source and target languages.The use of flexible parsing operations raises a couple of challengesthat are a major focus of our research. First, efficient decodingalgorithms are required for the models. Second, flexible parsingoperations allow the model to capture complex reordering phenomena,but in addition introduce many spurious possibilities. We areinvestigating the use of learned, probabilistic constraints based oninformation in the source-language sentence, or in a parse tree forthe source-language sentence, thereby incorporating syntacticinformation from the source language.The end goal of the project is to develop new models for translationthat improve the fluency or grammaticality of translations, improvethe degree to which semantic information (e.g., predicate-argumentstructure) is preserved in translation, and improve the treatment ofdiffering word orders between source and target languages.