Hierarchical Phrase-based Translation with Weighted Finite State Transducers – Bill Byrne (University of Cambridge)

November 24, 2009 all-day

HiFST is a lattice-based decoder for hierarchical phrase-based translation and alignment. The decoder is implemented with standard Weighted Finite-State Transducer (WFST) operations as an alternative to the well-known cube pruning procedure. We find that the use of WFSTs rather than k-best lists requires less pruning in translation search, resulting in fewer search errors, better parameter optimization, and improved translation performance. The direct generation of translation lattices in the target language can improve subsequent rescoring procedures, yielding further gains when applying long-span language models and Minimum Bayes Risk decoding.
Bill Byrne is a Reader in Information Engineering at the University of Cambridge.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
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Center for Language and Speech Processing