|Title:||Machine reading comprehension with deep neural networks|
|Subject:||Hong Kong Polytechnic University -- Dissertations|
Neural networks (Computer science)
|Department:||Department of Computing|
|Pages:||vii, 51 pages : color illustrations|
|Abstract:||Recently, neural networks have been very popular in solving many tasks and shows great progress in the field of Artificial Intelligence. In this thesis, steps towards Machine reading comprehension (MRC) are examined by applying deep neural networks with attention mechanism to large-scale datasets in order to develop the human-like ability to read passages and answer corresponding questions. We explore different kinds of MRC tasks based on different public datasets. Besides we analyze the different neural network models by using three popular datasets of Stanford Question Answering Dataset (SQuAD), Microsoft Machine Reading Comprehension Dataset (MS MARCO), Baidu Machine Reading Comprehension Dataset (DuReader). We describe the methodology for machine reading comprehension. Finally, we introduce a new neural network model EaR for multi-document MRC tasks and we make a comparison of our new model with the existing state-of-art models by conducting experiments on the realistic dataset of DuReader. The thesis is closed by the future application of machine reading comprehension in the real world.|
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|991022268443503411.pdf||For PolyU Staff & Students||1.21 MB||Adobe PDF||View/Open|
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