The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Open accessAbstract
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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“In this second category, previous work trains a re-ranking module which can traverse a graph made using Wikipedia hyperlinks [16, 100, 54, 14, 4, 44].”
From HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024) · cites this paper as [16]
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Self-supervised Learning: Generative or Contrastive
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Pre-trained models: Past, present and future
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Self-citation
Metadata
- Authors
- Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J.
- Affiliations
- Alibaba DAMO Academy (China) (CN)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 2694-2703
- ISSN
- 0736-587X (ISSN-L)
- 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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