Kevin Duh

Publications (7)0 Total impact

• Source

  Available from: aclweb.org

  Article: Divide and translate: improving long distance reordering in statistical machine translation

  Katsuhito Sudoh, Kevin Duh, Hajime Tsukada, Tsutomu Hirao, Masaaki Nagata
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  ABSTRACT: This paper proposes a novel method for long distance, clause-level reordering in statistical machine translation (SMT). The proposed method separately translates clauses in the source sentence and recon-structs the target sentence using the clause translations with non-terminals. The non-terminals are placeholders of embedded clauses, by which we reduce complicated clause-level reordering into simple word-level reordering. Its translation model is trained using a bilingual corpus with clause-level alignment, which can be au-tomatically annotated by our alignment algorithm with a syntactic parser in the source language. We achieved signifi-cant improvements of 1.4% in BLEU and 1.3% in TER by using Moses, and 2.2% in BLEU and 3.5% in TER by using our hierarchical phrase-based SMT, for the English-to-Japanese translation of re-search paper abstracts in the medical do-main.
  08/2010;
• Source

  Available from: mt-archive.info

  Article: N-best Reranking by Multitask Learning

  Kevin Duh, Katsuhito Sudoh, Hajime Tsukada, Hideki Isozaki, Masaaki Nagata
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  ABSTRACT: We propose a new framework for N-best reranking on sparse feature sets. The idea is to reformulate the reranking problem as a Multitask Learning problem, where each N-best list corresponds to a distinct task. This is motivated by the observation that N-best lists often show significant differ-ences in feature distributions. Training a single reranker directly on this heteroge-nous data can be difficult. Our proposed meta-algorithm solves this challenge by using multitask learning (such as ℓ 1 /ℓ 2 regularization) to discover common feature representations across N-best lists. This meta-algorithm is simple to implement, and its modular approach al-lows one to plug-in different learning algo-rithms from existing literature. As a proof of concept, we show statistically signifi-cant improvements on a machine transla-tion system involving millions of features.
  08/2010;
• Source

  Available from: aclweb.org

  Article: Head finalization: A simple reordering rule for sov languages

  Hideki Isozaki, Katsuhito Sudoh, Hajime Tsukada, Kevin Duh
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  ABSTRACT: English is a typical SVO (Subject-Verb-Object) language, while Japanese is a typ-ical SOV language. Conventional Statis-tical Machine Translation (SMT) systems work well within each of these language families. However, SMT-based translation from an SVO language to an SOV lan-guage does not work well because their word orders are completely different. Re-cently, a few groups have proposed rule-based preprocessing methods to mitigate this problem (Xu et al., 2009; Hong et al., 2009). These methods rewrite SVO sen-tences to derive more SOV-like sentences by using a set of handcrafted rules. In this paper, we propose an alternative single re-ordering rule: Head Finalization. This is a syntax-based preprocessing approach that offers the advantage of simplicity. We do not have to be concerned about part-of-speech tags or rule weights because the powerful Enju parser allows us to imple-ment the rule at a general level. Our ex-periments show that its result, Head Final English (HFE), follows almost the same order as Japanese. We also show that this rule improves automatic evaluation scores.
  08/2010;
• Source

  Available from: aclweb.org

  Conference Proceeding: Hierarchical Phrase-based Machine Translation with Word-based Reordering Model.

  Katsuhiko Hayashi, Hajime Tsukada, Katsuhito Sudoh, Kevin Duh, Seiichi Yamamoto
  COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 23-27 August 2010, Beijing, China; 01/2010
• Source

  Available from: ntt.co.jp

  Conference Proceeding: Automatic Evaluation of Translation Quality for Distant Language Pairs.

  Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, Hajime Tsukada
  Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, 9-11 October 2010, MIT Stata Center, Massachusetts, USA, A meeting of SIGDAT, a Special Interest Group of the ACL; 01/2010
• Source

  Available from: nii.ac.jp

  Article: Learning of Linear Ordering Problems and its Application to JE Patent Translation in NTCIR-9 PatentMT

  Shuhei Kondo, Mamoru Komachi, Yuji Matsumoto, Katsuhito Sudoh, Kevin Duh, Hajime Tsukada
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  ABSTRACT: This paper describes the patent translation system submit-ted for the NTCIR-9 PatentMT task. We applied the Lin-ear Ordering Problem (LOP) based reordering model [16] to Japanese-to-English translation to deal with the substantial difference in the word order between the two languages.
• Source

  Available from: ntt.co.jp

  Article: NTT-UT Statistical Machine Translation in NTCIR-9 PatentMT

  Katsuhito Sudoh, Kevin Duh, Hajime Tsukada, Masaaki Nagata, Xianchao Wu, Takuya Matsuzaki, Jun ' Ichi Tsujii
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  ABSTRACT: This paper describes details of the NTT-UT system in NTCIR-9 PatentMT task. One of its key technology is system com-bination; the final translation hypotheses are chosen from n-bests by different SMT systems in a Minimum Bayes Risk (MBR) manner. Each SMT system includes different tech-nology: syntactic pre-ordering, forest-to-string translation, and using external resources for domain adaptation and tar-get language modeling.