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

In this paper, we present hybrid decoding
— a novel statistical machine translation
(SMT) decoding paradigm using multiple
SMT systems. In our work, in addition
to component SMT systems, system
combination method is also employed
in generating partial translation hypotheses
throughout the decoding process, in
which smaller hypotheses generated by
each component decoder and hypotheses
combination are used in the following decoding
steps to generate larger hypotheses.
Experimental results on NIST evaluation
data sets for Chinese-to-English machine
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.