A general-purpose rule extractor for SCFG-based machine translation

Authors:
Greg Hanneman;Michelle Burroughs;Alon Lavie
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Year:
2011

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Abstract

We present a rule extractor for SCFG-based MT that generalizes many of the contraints present in existing SCFG extraction algorithms. Our method's increased rule coverage comes from allowing multiple alignments, virtual nodes, and multiple tree decompositions in the extraction process. At decoding time, we improve automatic metric scores by significantly increasing the number of phrase pairs that match a given test set, while our experiments with hierarchical grammar filtering indicate that more intelligent filtering schemes will also provide a key to future gains.