Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation

Shin Kanouchi, Katsuhito Sudoh, Mamoru Komachi


Abstract
This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network. Lexicalized reordering suffers from reordering ambiguity, data sparseness and noises in a phrase table. Previous neural reordering model is successful to solve the first and second problems but fails to address the third one. Therefore, we propose new features using phrase translation and word alignment to construct phrase vectors to handle inherently noisy phrase translation pairs. The experimental results show that our proposed method improves the accuracy of phrase reordering. We confirm that the proposed method works well with phrase pairs including NULL alignments.
Anthology ID:
W16-4607
Volume:
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
WAT | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
94–103
Language:
URL:
https://aclanthology.org/W16-4607
DOI:
Bibkey:
Cite (ACL):
Shin Kanouchi, Katsuhito Sudoh, and Mamoru Komachi. 2016. Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 94–103, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation (Kanouchi et al., 2016)
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PDF:
https://aclanthology.org/W16-4607.pdf