文章基本信息

标题：Multiple Translation-Engine-based Hypotheses and Edit-Distance-based Rescoring for a Greedy Decoder for Statistical Machine Translation
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作者：Michael Paul ; Eiichiro Sumita ; Seiichi Yamamoto 等
期刊名称：IPSJ Digital Courier
电子版ISSN：1349-7456
出版年度：2005
卷号：1
页码：561-575
DOI：10.2197/ipsjdc.1.561
出版社：Information Processing Society of Japan
摘要：This paper extends a greedy decoder for statistical machine translation (SMT), which searches for an optimal translation by using SMT models starting from a decoder seed, i.e., the source language input paired with an initial translation hypothesis. First, the outputs generated by multiple translation engines are utilized as the initial translation hypotheses, whereby their variations reduce local optima problems inherent in the search. Second, a rescoring method based on the edit-distance between the initial translation hypothesis and the outputs of the decoder is used to compensate for problems of conventional greedy decoding solely based on statistical models. Our approach is evaluated for the translation of dialogues in the travel domain, and the results show that it drastically improves translation quality.