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.
Highlights
The general form of reading comprehension is that the tester answers the relevant questions of the article by reading an article and understanding the meaning of the article
Compared with the above model, the model designed in this paper introduces a self-attention mechanism when coding, so that the model can better learn the information contained in the sentence
The BiDMF designed in this paper is a multi-stage hierarchical model, to make the model better understand the meaning of the context and reduce the training process of the information of the loss
Summary
The general form of reading comprehension is that the tester answers the relevant questions of the article by reading an article and understanding the meaning of the article. From the research in recent years, currently, the attention mechanism of the machine reading comprehension model is single-pass, which is more common, based on deep learning. Multiple iterations of attention mechanism are introduced to simulate the habit of repeated reading by humans in the input layer and the attention layer of the network model so that the network has better learning ability. In 2015, Hermann et al first introduced attention mechanisms into the tasks of machine reading comprehension. Bi-Directional Attention Flow (BiDAF) is a deep learning model for machine reading comprehension proposed by Minjoon Seo et al [15] Compare with previous work, BiDAF's biggest improvement is the introduction of a bidirectional attention mechanism in the Interaction layer. The introduction of an additional attention mechanism can reduce the loss of information, and it can make the generated answers more accurate
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