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 avoid-ing the need for syntactic analysis of the less re-source 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 syntax-based heuristics which transform the target lan-guage (e.g. English) into a form more closely resem-bling the source language, and then by using stan-dard alignment methods to align the transformed bitext. We present experimental results under vari-able resource conditions. The method improves word alignment performance for language pairs such as English-Korean and English-Hindi, which exhibit longer-distance syntactic divergences.