Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to e ectively exploit and then reason over the relation among queries, documents, and answers. Di erent from previous approaches using a xed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.
REF propose a multi-hop attention model that used reinforcement learning to dynamically determine when to stop digesting intermediate information and produce an answer.
ReasoNet: Learning to Stop Reading in Machine Comprehension
KDD '17
Computer Science