Bi-directional CognitiveThinking Network for Machine Reading Comprehension
Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie, Jing Yu, Yajing Sun, Xiangpeng Wei
Abstract
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.- Anthology ID:
- 2020.coling-main.235
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2613–2623
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.235
- DOI:
- 10.18653/v1/2020.coling-main.235
- Cite (ACL):
- Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie, Jing Yu, Yajing Sun, and Xiangpeng Wei. 2020. Bi-directional CognitiveThinking Network for Machine Reading Comprehension. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2613–2623, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Bi-directional CognitiveThinking Network for Machine Reading Comprehension (Peng et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.coling-main.235.pdf
- Data
- DuReader