Adaptation of Reordering Models for Statistical Machine Translation
Boxing Chen, George Foster, Roland Kühn · NPARC · 2013
Previous research on domain adaptation (DA) for statistical machine translation (SMT) has mainly focused on the translation model (TM) and the language model (LM). To the best of our knowledge, there is no previous work on reordering model (RM) adaptation for phrase-based SMT. In this paper, we demonstrate that mixture model adaptation of a lexical-ized RM can significantly improve SMT per-formance, even when the system already con-tains a domain-adapted TM and LM. We find that, surprisingly, different training corpora can vary widely in their reordering character-istics for particular phrase pairs. Furthermore, particular training corpora may be highly suit-able for training the TM or the LM, but unsuit-able for training the RM, or vice versa, so mix-ture weights for these models should be esti-mated separately. An additional contribution of the paper is to propose two improvements to mixture model adaptation: smoothing the in-domain sample, and weighting instances by document frequency. Applied to mixture RMs in our experiments, these techniques (es-pecially smoothing) yield significant perfor-mance improvements. 1