English-Chinese Knowledge Base Translation with Neural Network

Xiaocheng Feng, Duyu Tang, Bing Qin, Ting Liu


Abstract
Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language like Chinese. To expand Chinese KB by leveraging English KB resources, an effective way is to translate English KB (source) into Chinese (target). In this direction, two major challenges are to model triple semantics and to build a robust KB translator. We address these challenges by presenting a neural network approach, which learns continuous triple representation with a gated neural network. Accordingly, source triples and target triples are mapped in the same semantic vector space. We build a new dataset for English-Chinese KB translation from Freebase, and compare with several baselines on it. Experimental results show that the proposed method improves translation accuracy compared with baseline methods. We show that adaptive composition model improves standard solution such as neural tensor network in terms of translation accuracy.
Anthology ID:
C16-1276
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:
2935–2944
Language:
URL:
https://aclanthology.org/C16-1276
DOI:
Bibkey:
Cite (ACL):
Xiaocheng Feng, Duyu Tang, Bing Qin, and Ting Liu. 2016. English-Chinese Knowledge Base Translation with Neural Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2935–2944, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
English-Chinese Knowledge Base Translation with Neural Network (Feng et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1276.pdf