Cited by 224Topic ModelingInformation Retrieval and Search BehaviorMultimodal Machine Learning Applications

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Cognitive Graph for Multi-Hop Reading Comprehension at Scale

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Ding, Ming, Zhou, Chang, Chen, Qibin, Yang, Hongxia, Tang, Jie·2019·Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

DOI 10.18653/v1/p19-1259

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Abstract

We propose a new CogQA framework for multi-hop question answering in web-scale documents. Inspired by the dual process theory in cognitive science, the framework gradually builds a \textit{cognitive graph} in an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides explainable reasoning paths. Specifically, our implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning joint $F_1$ score of 34.9 on the leaderboard, compared to 23.6 of the best competitor.

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Authors: Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J.
Affiliations: Alibaba DAMO Academy (China) (CN)
ORCID: Ding, Ming
Zhou, Chang
Chen, Qibin
Yang, Hongxia
Tang, Jie
Venue: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Publisher: Association for Computational Linguistics
Published: 2019
Pages: 2694-2703
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/p19-1259
OpenAlex: W2962922117
Cited by: 222
References: 32
Citations by year: 2026: 8 · 2025: 13 · 2024: 19 · 2023: 28 · 2022: 35 · 2021: 60 · 2020: 39 · 2019: 20
FWCI: 14.73
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Natural Science Foundation of China (61836013, 61825602)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords: question answering, cognitive graphs, dual-process theory, System 1 and System 2, explicit reasoning, HotpotQA, graph neural networks, BERT

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Cognitive Graph for Multi-Hop Reading Comprehension at Scale

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