Cognitive Graph for Multi-Hop Reading Comprehension at Scale

ACL 2019

Details
Abstract: We propose a new CogQA framework for multi-hop reading comprehension question answering in web-scale documents. Founded on the dual process theory in cognitive science, the framework gradually builds a 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 F1 score of 34.9 on the leaderboard, compared to 23.1 of the best competitor. Authors: Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang (Tsinghua University, Alibaba Group)

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