Fast consensus hypothesis regeneration for machine translation

Authors:
Boxing Chen;George Foster;Roland Kuhn
Affiliations:
National Research Council Canada, Gatineau (Québec), Canada;National Research Council Canada, Gatineau (Québec), Canada;National Research Council Canada, Gatineau (Québec), Canada
Venue:
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Year:
2010

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Abstract

This paper presents a fast consensus hypothesis regeneration approach for machine translation. It combines the advantages of feature-based fast consensus decoding and hypothesis regeneration. Our approach is more efficient than previous work on hypothesis regeneration, and it explores a wider search space than consensus decoding, resulting in improved performance. Experimental results show consistent improvements across language pairs, and an improvement of up to 0.72 BLEU is obtained over a competitive single-pass baseline on the Chinese-to-English NIST task.