@inproceedings{peng-etal-2020-bi,
title = "Bi-directional {C}ognitive{T}hinking Network for Machine Reading Comprehension",
author = "Peng, Wei and
Hu, Yue and
Xing, Luxi and
Xie, Yuqiang and
Yu, Jing and
Sun, Yajing and
Wei, Xiangpeng",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.235",
doi = "10.18653/v1/2020.coling-main.235",
pages = "2613--2623",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Bi-directional CognitiveThinking Network for Machine Reading Comprehension
%A Peng, Wei
%A Hu, Yue
%A Xing, Luxi
%A Xie, Yuqiang
%A Yu, Jing
%A Sun, Yajing
%A Wei, Xiangpeng
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F peng-etal-2020-bi
%X 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.
%R 10.18653/v1/2020.coling-main.235
%U https://aclanthology.org/2020.coling-main.235
%U https://doi.org/10.18653/v1/2020.coling-main.235
%P 2613-2623
Markdown (Informal)
[Bi-directional CognitiveThinking Network for Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.235) (Peng et al., COLING 2020)
ACL