Key points are not available for this paper at this time.
This paper addresses the problem of producing 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 diverse 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 performance on these tasks, especially for sentences that are difficult for MT.
Gimpel et al. (Tue,) studied this question.