A Study of the Tasks and Models in Machine Reading Comprehension
It provides a comprehensive overview for researchers in NLP, but is incremental as it synthesizes existing work without new experimental results.
This report surveys existing tasks and models in Machine Reading Comprehension, reviewing dataset collections, neural network architectures, and knowledge incorporation approaches, and proposes open problems for future research.
To provide a survey on the existing tasks and models in Machine Reading Comprehension (MRC), this report reviews: 1) the dataset collection and performance evaluation of some representative simple-reasoning and complex-reasoning MRC tasks; 2) the architecture designs, attention mechanisms, and performance-boosting approaches for developing neural-network-based MRC models; 3) some recently proposed transfer learning approaches to incorporating text-style knowledge contained in external corpora into the neural networks of MRC models; 4) some recently proposed knowledge base encoding approaches to incorporating graph-style knowledge contained in external knowledge bases into the neural networks of MRC models. Besides, according to what has been achieved and what are still deficient, this report also proposes some open problems for the future research.