Improving Bitext Word Alignments via Syntax-based Reordering of English

We present an improved method for automated word alignment of parallel texts which takes advantage of knowledge of syntactic divergences, while avoiding the need for syntactic analysis of the less resource rich language, and retaining the robustness of syntactically agnostic approaches such as the IBM word alignment models. We achieve this by using simple, easily-elicited knowledge to produce syntaxbased heuristics which transform the target language (e.g. English) into a form more closely resembling the source language, and then by using standard alignment methods to align the transformed bitext. We present experimental results under variable resource conditions. The method improves word alignment performance for language pairs such as English-Korean and English-Hindi, which exhibit longer-distance syntactic divergences.