Discriminative Word Alignment with a Function Word Reordering Model

Hendra Setiawan, Christopher Dyer, Philip Resnik

Abstract: We address the modeling, parameter estimation and search challenges that arise from the introduction of reordering models that capture non-local reordering in alignment modeling. In particular, we introduce several reordering models that utilize (pairs of) function words as contexts for alignment reordering. To address the parameter estimation challenge, we propose to estimate these reordering models from a relatively small amount of manually-aligned corpora. To address the search challenge, we devise an iterative local search algorithm that stochastically explores reordering possibilities. By capturing non-local reordering phenomena, our proposed alignment model bears a closer resemblance to state-of-the-art translation model. Empirical results show significant improvements in alignment quality as well as in translation performance over baselines in a large-scale Chinese-English translation task.