Paper: A Systematic Exploration of Diversity in Machine Translation

ACL ID D13-1111
Title A Systematic Exploration of Diversity in Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

This paper addresses the problem of produc- ing a diverse set of plausible translations. We present a simple procedure that can be used with any statistical machine translation (MT) system. We explore three ways of using di- verse translations: (1) system combination, (2) discriminative reranking with rich features, and (3) a novel post-editing scenario in which multiple translations are presented to users. We find that diversity can improve perfor- mance on these tasks, especially for sentences that are difficult for MT.