Abstract
We propose a new CogQA framework for
multi-hop 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. Specifi-
cally, our implementation1 based on BERT
and graph neural network (GNN) 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 23.6 of
the best competitor