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 (23)
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Teaching Machines to Read and Comprehend
- Convolutional Neural Networks for Sentence Classification
- SQuAD: 100,000+ Questions for Machine Comprehension of Text
- Reasoning about Entailment with Neural Attention
- The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
- Dynamic Coattention Networks For Question Answering
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Pointer Sentinel Mixture Models
- Machine Comprehension Using Match-LSTM and Answer Pointer
- QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
- ReasoNet: Learning to Stop Reading in Machine Comprehension
- Adversarial Examples for Evaluating Reading Comprehension Systems
- Depthwise Separable Convolutions for Neural Machine Translation
- A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
- RACE: Large-scale ReAding Comprehension Dataset From Examinations
- NewsQA: A Machine Comprehension Dataset
- Generating Wikipedia by Summarizing Long Sequences
- MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge
- Learning Natural Language Inference with LSTM
- Making Neural QA as Simple as Possible but not Simpler
- Large-scale Cloze Test Dataset Created by Teachers
Cited by in corpus (9)
- Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
- A Survey on Explainability in Machine Reading Comprehension
- Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
- Question Answering is a Format; When is it Useful?
- RuBQ: A Russian Dataset for Question Answering over Wikidata
- A Survey of Document Grounded Dialogue Systems (DGDS)
- A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
- Machine Reading of Hypotheses for Organizational Research Reviews and Pre-trained Models via R Shiny App for Non-Programmers
- Building a Question and Answer System for News Domain