Are very large N-best lists useful for SMT?

Abstract

This paper describes an efficient method to extract large n-best lists from a word graph produced by a statistical machine translation system. The extraction is based on the k shortest paths algorithm which is efficient even for very large k. We show that, although we can generate large amounts of distinct translation hypotheses, these numerous candidates are not able to significantly improve overall system performance. We conclude that large n-best lists would benefit from better discriminating models.