A Study of the Tasks and Models in Machine Reading Comprehension
- URL: http://arxiv.org/abs/2001.08635v1
- Date: Thu, 23 Jan 2020 16:11:44 GMT
- Title: A Study of the Tasks and Models in Machine Reading Comprehension
- Authors: Chao Wang
- Abstract summary: This report reviews some representative simple-reasoning and complex-reasoning MRC tasks.
It also proposes some open problems for the future research.
- Score: 3.6985039575807246
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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