Fast and optimal decoding for machine translation

Authors:
Ulrich Germann;Michael Jahr;Kevin Knight;Daniel Marcu;Kenji Yamada
Affiliations:
Information Sciences Institute and Department of Computer Science, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA;Department of Computer Science, Stanford University, Stanford, CA;Information Sciences Institute and Department of Computer Science, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA;Information Sciences Institute and Department of Computer Science, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA;Information Sciences Institute and Department of Computer Science, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA
Venue:
Artificial Intelligence
Year:
2004

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Abstract

A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder's job is to find the translation that is most likely according to a set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. Unfortunately, examining more of the space leads to unacceptably slow decodings.In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast but non-optimal greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.