Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation

Abstract

Preordering of a source language sentence to match target word order has proved to be useful for improving machine translation systems. Previous work has shown that a reordering model can be learned from high quality manual word alignments to improve machine translation performance. In this paper, we focus on further improving the performance of the reordering model (and thereby machine translation) by using a larger corpus of sentence aligned data for which manual word alignments are not available but automatic machine generated alignments are available. The main challenge we tackle is to generate quality data for training the reordering model in spite of the machine alignments being noisy. To mitigate the effect of noisy machine alignments, we propose a novel approach that improves reorderings produced given noisy alignments and also improves word alignments using information from the reordering model. This approach generates alignments that are 2.6 f-Measure points better than a baseline supervised aligner. The data generated allows us to train a reordering model that gives an improvement of 1.8 BLEU points on the NIST MT-08 Urdu-English evaluation set over a reordering model that only uses manual word alignments, and a gain of 5.2 BLEU points over a standard phrase-based baseline. © 2013 Association for Computational Linguistics.