Efficient Decoding for Statistical Machine Translation with a Fully Expanded WFST Model.
Hajime Tsukada, Masaaki Nagata · 2004
This paper proposes a novel method to compile sta-tistical models for machine translation to achieve efficient decoding. In our method, each statistical submodel is represented by a weighted finite-state transducer (WFST), and all of the submodels are ex-panded into a composition model beforehand. Fur-thermore, the ambiguity of the composition model is reduced by the statistics of hypotheses while de-coding. The experimental results show that the pro-posed model representation drastically improves the efficiency of decoding compared to the dynamic composition of the submodels, which corresponds to conventional approaches. 1