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Title: Distortion Model Based on Word Sequence Labeling for Statistical Machine Translation
Authors: Goto, Isao
Utiyama, Masao
Sumita, Eiichiro
Tamura, Akihiro
Kurohashi, Sadao  kyouindb  KAKEN_id
Author's alias: 黒橋, 禎夫
Keywords: Distortion model
machine translation
Issue Date: 1-Feb-2014
Publisher: Association for Computing Machinery (ACM)
Journal title: ACM Transactions on Asian Language Information Processing
Volume: 13
Issue: 1
Thesis number: 2
Abstract: This article proposes a new distortion model for phrase-based statistical machine translation. In decoding, a distortion model estimates the source word position to be translated next (subsequent position; SP) given the last translated source word position (current position; CP). We propose a distortion model that can simultaneously consider the word at the CP, the word at an SP candidate, the context of the CP and an SP candidate, relative word order among the SP candidates, and the words between the CP and an SP candidate. These considered elements are called rich context. Our model considers rich context by discriminating label sequences that specify spans from the CP to each SP candidate. It enables our model to learn the effect of relative word order among SP candidates as well as to learn the effect of distances from the training data. In contrast to the learning strategy of existing methods, our learning strategy is that the model learns preference relations among SP candidates in each sentence of the training data. This leaning strategy enables consideration of all of the rich context simultaneously. In our experiments, our model had higher BLUE and RIBES scores for Japanese-English, Chinese-English, and German-English translation compared to the lexical reordering models.
Rights: Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. 2014 Copyright is held by the author/owner(s).
DOI(Published Version): 10.1145/2537128
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