Paper: Hybrid Decoding: Decoding with Partial Hypotheses Combination over Multiple SMT Systems

ACL ID C10-2025
Title Hybrid Decoding: Decoding with Partial Hypotheses Combination over Multiple SMT Systems
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

In this paper, we present hybrid decod- ing — a novel statistical machine transla- tion (SMT) decoding paradigm using mul- tiple SMT systems. In our work, in ad- dition to component SMT systems, sys- tem combination method is also employed in generating partial translation hypothe- ses throughout the decoding process, in which smaller hypotheses generated by each component decoder and hypotheses combination are used in the following de- coding steps to generate larger hypothe- ses. Experimental results on NIST evalu- ation data sets for Chinese-to-English ma- chine translation (MT) task show that our method can not only achieve significant improvements over individual decoders, but also bring substantial gains compared with a state-of-the-art word-level system combination method.