System Description of bjtu_nlp Neural Machine Translation System

Shaotong Li, JinAn Xu, Yufeng Chen, Yujie Zhang


Abstract
This paper presents our machine translation system that developed for the WAT2016 evalua-tion tasks of ja-en, ja-zh, en-ja, zh-ja, JPCja-en, JPCja-zh, JPCen-ja, JPCzh-ja. We build our system based on encoder–decoder framework by integrating recurrent neural network (RNN) and gate recurrent unit (GRU), and we also adopt an attention mechanism for solving the problem of information loss. Additionally, we propose a simple translation-specific approach to resolve the unknown word translation problem. Experimental results show that our system performs better than the baseline statistical machine translation (SMT) systems in each task. Moreover, it shows that our proposed approach of unknown word translation performs effec-tively improvement of translation results.
Anthology ID:
W16-4608
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:
104–110
Language:
URL:
https://www.aclweb.org/anthology/W16-4608
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4608.pdf