Generalizing local and non-local word-reordering patterns for syntax-based machine translation

Syntactic word reordering is essential for translations across different grammar structures between syntactically distant language-pairs. In this paper, we propose to embed local and non-local word reordering decisions in a synchronous context free grammar, and leverages the grammar in a chart-based decoder. Local word-reordering is effectively encoded in Hiero-like rules; whereas non-local word-reordering, which allows for long-range movements of syntactic chunks, is represented in tree-based reordering rules, which contain variables correspond to sourceside syntactic constituents. We demonstrate how these rules are learned from parallel corpora. Our proposed shallow Tree-to-String rules show significant improvements in translation quality across different test sets. © 2008 Association for Computational Linguistics.