Statistical syntax-directed translation with extended domain of locality

Abstract

In syntax-directed translation, the sourcelanguage input is first parsed into a parsetree, which is then recursively converted into a string in the target-language. We model this conversion by an extended treeto- string transducer that has multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear framework in order to incorporate other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for n- gram rescoring. Preliminary experiments on English-to-Chinese translation show a significant improvement in terms of translation quality compared to a state-of-theart phrase-based system. © 2006 The Association for Machine Translation in the Americas.