Consensus Attention-based Neural Networks for Chinese Reading Comprehension

Yiming Cui, Ting Liu, Zhipeng Chen, Shijin Wang, Guoping Hu


Abstract
Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children’s Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.
Anthology ID:
C16-1167
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1777–1786
Language:
URL:
https://aclanthology.org/C16-1167
DOI:
Bibkey:
Cite (ACL):
Yiming Cui, Ting Liu, Zhipeng Chen, Shijin Wang, and Guoping Hu. 2016. Consensus Attention-based Neural Networks for Chinese Reading Comprehension. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1777–1786, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Consensus Attention-based Neural Networks for Chinese Reading Comprehension (Cui et al., COLING 2016)
Copy Citation:
PDF:
https://aclanthology.org/C16-1167.pdf
Data
CBT