Paper: Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices

ACL ID P09-1019
Title Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decod- ing are used in most current state-of-the- art Statistical Machine Translation (SMT) systems. The algorithms were originally developed to work with N-best lists of translations, and recently extended to lat- tices that encode many more hypotheses than typical N-best lists. We here extend lattice-based MERT and MBR algorithms to work with hypergraphs that encode a vast number of translations produced by MT systems based on Synchronous Con- text Free Grammars. These algorithms are more efficient than the lattice-based versions presented earlier. We show how MERT can be employed to optimize pa- rameters for MBR decoding. Our exper- iments show speedups from MERT and MBR as well as performance improve- ments from MBR decodi...