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

ACL ID P13-1125
Title Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Preordering of a source language sentence to match target word order has proved to be useful for improving machine transla- tion systems. Previous work has shown that a reordering model can be learned from high quality manual word alignments to improve machine translation perfor- mance. In this paper, we focus on further improving the performance of the reorder- ing model (and thereby machine transla- tion) by using a larger corpus of sentence aligned data for which manual word align- ments are not available but automatic ma- chine generated alignments are available. The main challenge we tackle is to gen- erate quality data for training the reorder- ing model in spite of the machine align- ments being noisy. To mitigate the effect of noisy machine alignments, we propose a novel approach t...