Machine Reading Comprehension Based on Multi-headed attention Model

Abstract

Machine Reading Comprehension (MRC) refers to the task that aims to read the context through the machine and answer the question about the original text, which needs to be modeled in the interaction between the context and the question. Recently, attention mechanisms in deep learning have been successfully extended to MRC tasks. In general, the attention-based approach is to focus attention on a small part of the context and to generalize it using a fixed-size vector. This paper introduces a network of attention from coarse to fine, which is a multi-stage hierarchical process. Firstly, the context and questions are encoded by bi-directional LSTM RNN; Then, more accurate interaction information is obtained after multiple iterations of the attention mechanism; Finally, a cursor-based approach is used to predicts the answer at the beginning and end of the original text. Experimental evaluation of shows that the BiDMF (Bi-Directional Multi-Attention Flow) model designed in this paper achieved 34.1% BLUE4 value and 39.5% Rouge-L value on the test set.