Beam-Width Adaptation for Hierarchical Phrase-Based Translation

In terms of translation quality, hierarchical phrase-based translation model (Hiero) has shown state-of-the-art performance in various translation tasks. However, the slow decoding speed of Hiero prevents it from effective deployment in online scenarios. In this paper, we propose beam-width adaptation strategies to speed up Hiero decoding. We learn maximum entropy models to evaluate the quality of each span and then predict the optimal beam-width for it. The empirical studies on Chinese-to-English translation tasks show that, even in comparison with a competitive baseline which employs well designed cube pruning, our approaches still double the decoding speed without compromising translation quality. The approaches have already been applied to an online commercial translation system.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000342990000019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Theory & MethodsEICPCI-S(ISTP)