Read, Attend, and Exclude: Multi-Choice Reading Comprehension by Mimicking Human Reasoning Process

Abstract

Multi-Choice Reading Comprehension∼(MCRC) is an essential task where a machine selects the correct answer from multiple choices given a context document and a corresponding question. Existing methods usually make predictions based on a single-round reasoning process with the attention mechanism, however, this may be insufficient for tasks that require a more complex reasoning process. To effectively comprehend the context and select the correct answer from different perspectives, we propose the Read-Attend-Exclude (RAE) model which is motivated by what human readers do for MCRC in multi-rounds reasoning process. Specifically, the RAE model includes four components: the Scan Reading Module, the Attended Intensive Reading Module, the Answer Exclusion Module, and the Gated Fusion Module that makes the final decisions collectively based on the aforementioned three modules. Extensive experiments demonstrate the strong results of the proposed model on the DREAM dataset and the effectiveness of all proposed modules.