A Multiple Granularity Co-Reasoning Model for Multi-choice Reading Comprehension
Hang Miao, Ruifang Liu, Sheng Gao · 2019
We propose a multi-granularity co-reasoning model for multi-choice reading comprehension task, which aims to select the correct option based on the interaction between passage, question and candidate options. Firstly, we introduce a multiple granularity text matching module to interact passage with question and each option. We take advantage of information extracted from diverse semantic spaces to conduct more extensive matching between text sequences. With this help, we could better match the passage against the question and each option to gather relevant information. Furthermore, we employ a multi-sentence co-reasoning module for sentence inference across multiple sentences. Specifically, we utilize 1D Convolutional Neural Network (1D-CNN) with different kernel sizes and self-attentive Recurrent Neural Network (RNN) to model the relationships of relevant sentences. This module could better synthesize and aggregate sentence-level evidence to make decisions. Experimental results demonstrate that our proposed model achieves state-of-the-art performance for single models on the RACE dataset.