Cognitive Graph For Multi-hop Reading Comprehension At Scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 205 citations
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.