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

Authors:

YoungSook HwangAuthors Info & Claims

ICUIMC '11: Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication

Article No.: 2, Pages 1 - 5

https://doi.org/10.1145/1968613.1968616

Published: 21 February 2011 Publication History

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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.

Index Terms

Predicate-argument reordering based on learning to rank for English-Korean machine translation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation

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Published In

ICUIMC '11: Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication

February 2011

959 pages

ISBN:9781450305716

DOI:10.1145/1968613

General Chairs:
Suk-Han Lee
Sungkyunkwan University, Korea
,
Lajos Hanzo
University of Southampton
,
Program Chairs:
Min Young Chung
Sungkyunkwan University, Korea
,
Sang-Won Lee
Sungkyunkwan University, Korea
,
Kwangsu Cho
University of Missouri

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 February 2011

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ICUIMC '11: The 5th International Conference on Ubiquitous Information Management and Communication

February 21 - 23, 2011

Seoul, Korea

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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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Recommendations

Improving phrase-based statistical machine translation with morphosyntactic transformation

Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation

A Reordering Model for Phrase-Based Machine Translation

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