Paper: Joint Decoding with Multiple Translation Models

ACL ID P09-1065
Title Joint Decoding with Multiple Translation Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

Current SMT systems usually decode with single translation models and cannot ben- efit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multi- ple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the trans- lation hypergraphs they produce individu- ally. Therefore, one model can share trans- lations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint de- coding achieves an absolute improvement of 1.5 BLEU points over individual decod- ing.