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Predicate-argument reordering based on learning to rank for English-Korean machine translation

Published: 21 February 2011 Publication History

Abstract

In this paper, we propose a method of learning predicate-argument structure reordering, and present its effect on machine translation. The method takes two steps; first, it extracts generalized predicate-argument structure reordering rules using a source sentence parse tree from a parallel corpus. Second, it trains a model based on learning to rank framework to select the most relevant reordering rule based on source language context features. The learned model is used to restructure a source sentence in order to have similar word order with a target sentence. In our experiments on English-to-Korean machine translation, the proposed method achieves significant improvements in BLEU score, from 19.68 to 21.84.

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cover image ACM Conferences
ICUIMC '11: Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
February 2011
959 pages
ISBN:9781450305716
DOI:10.1145/1968613
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 February 2011

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Author Tags

  1. learning to rank
  2. machine translation
  3. predicate-argument
  4. preprocessing
  5. reordering

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  • Research-article

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ICUIMC '11
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ICUIMC '11 Paper Acceptance Rate 135 of 534 submissions, 25%;
Overall Acceptance Rate 251 of 941 submissions, 27%

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