Article details

Research area
Natural language & AI

International Workshop on Spoken Language Translation, Kyoto, Japan,


Wade Shen, Tim Anderson

An efficient graph search decoder for phrase-based statistical machine translation


In this paper we describe an efficient implementation of a graph search algorithm for phrase-based statistical machine translation. Our goal was to create a decoder that could be used for both our research system and a real-time speech-to-speech machine translation demonstration system. The search algorithm is based on a Viterbi graph search with an A* heuristic. We were able to increase the speed of our de­coder substantially through the use of on-the-fly beam prun­ing and other algorithmic enhancements. The decoder sup­ports a variety of reordering constraints as well as arbitrary n-gram decoding. In addition, we have implemented disk based translation models and a messaging interface to communi­cate with other components for use in our real-time speech translation system.

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