Machine Reading Comprehension: a Literature Review
arXiv:1907.01686
Abstract
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent advances in MRC, mainly focusing on two aspects (i.e., corpus and techniques). The specific characteristics of various MRC corpus are listed and compared. The main ideas of some typical MRC techniques are also described.
46 pages, preprint version
References in corpus (8)
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Convolutional Neural Networks for Sentence Classification
- Pointer Sentinel Mixture Models
- Machine Comprehension Using Match-LSTM and Answer Pointer
- Adversarial Examples for Evaluating Reading Comprehension Systems
- Depthwise Separable Convolutions for Neural Machine Translation
- NewsQA: A Machine Comprehension Dataset
- Generating Wikipedia by Summarizing Long Sequences