A Character-Aware Encoder for Neural Machine Translation

Zhen Yang, Wei Chen, Feng Wang, Bo Xu


Abstract
This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn’t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.
Anthology ID:
C16-1288
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
3063–3070
Language:
URL:
https://aclanthology.org/C16-1288
DOI:
Bibkey:
Cite (ACL):
Zhen Yang, Wei Chen, Feng Wang, and Bo Xu. 2016. A Character-Aware Encoder for Neural Machine Translation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3063–3070, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
A Character-Aware Encoder for Neural Machine Translation (Yang et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1288.pdf