MRC.md

Machine Reading Comprehension

Literature

• English Machine Reading Comprehension Datasets: A Survey
• RCPapers: Must-read papers on Machine Reading Comprehension, by THUNLP.
• Reading-Comprehension-Question-Answering-Papers: Survey on Machine Reading Comprehension, by Xanh Ho.
• MRC_book: 《机器阅读理解：算法与实践》代码, by Chenguang Zhu.
• Multi-hopRC: 多跳阅读理解相关论文, by Jiaan Wang.
• Sewon Min (민세원) personal homepage

Classic Model（经典模型）

• R-NET 2017: paper|code
• DrQA 2017: paper|code
• BiDAF 2018: paper|code|homepage
• QANet 2018: paper|code
• BERT-MRC(base) 2018: paper|code(multi-choices)|code(question-answering)
• NumNet 2019: paper|code

Variations

• Common-Sense MRC/QA（常识型任务）
  • QASC:task|再下一城！HFL打破人类水平荣登常识推理挑战赛QASC榜首
  • OpenBookQA:task|单模型常识推理首超人类！HFL登顶OpenBookQA挑战赛
  • 常识问答研究综述 | 专知 2023-08-19
• Dialogue MRC/QA（对话型任务）
  • 面向对话的机器阅读理解任务（Dialogue MRC）相关论文整理
• Explainability in MRC/QA
  • ExpMRC: explainability evaluation for machine reading comprehension (2022)
  • A Survey on Explainability in Machine Reading Comprehension (2020)
• Knowledge Graph Question Answering
  • KGQA
• Logical Reasoning（逻辑推理型任务）
  • 赛尔笔记 | 逻辑推理阅读理解任务及方法
  • ReClor：一个需要逻辑推理的数据集
  • 从Bengio演讲发散开来：探讨逻辑推理与机器学习
• Multi-Hop MRC/QA（多跳推理型任务）
  • PathNet 2018: paper|code
  • CogQA 2019: homepage|paper|code
  • DecompRC 2019: paper|code|demo
  • SAE 2020: paper|code

Datasets & Competitions

• CAIL: China AI Law Challenge(中国法研杯司法人工智能挑战赛(含阅读理解赛道)).
• C³: Multiple-Choice Chinese Machine Reading Comprehension Dataset.
• ChID: A Large-scale Chinese IDiom Dataset for Cloze Test.
• CLICR: Machine reading comprehension on CLInical Case Reports.
• CLUE阅读理解排行榜
• CMRC
  • CMRC 2017: The 1st Evaluation Workshop on Chinese Machine Reading Comprehension.
  • CMRC 2018: A Span-Extraction Dataset for Chinese Machine Reading Comprehension.
  • CMRC 2019: A Sentence Cloze Dataset for Chinese Machine Reading Comprehension.
  • CMRC 2020: Challenge of AI in Law (CAIL) for Chinese Machine Reading Comprehension.
  • CMRC 2022: A Weak-Supervised Explainability-focused Dataset (from ExpMRC) for Chinese Machine Reading Comprehension.
• CoQA: A Conversational Question Answering Challenge.
• DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension.
• DRCD: Delta Reading Comprehension Dataset.
• DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
• DuReader from 千言（LUGE）: a Chinese Machine Reading Comprehension Dataset from Real-world Applications.
  • MRC_Competition_Dureader
• ExpMRC: Explainability Evaluation for Machine Reading Comprehension. 哈工大讯飞联合实验室发布可解释性阅读理解评测集ExpMRC
• FinQA(paper): A Dataset of Numerical Reasoning over Financial Data.
• FriendsQA: Open-Domain Question Answering on TV Show Transcripts.
• Hotpot: A Dataset for Diverse, Explainable Multi-hop Question Answering.
• insuranceQA: A Question Answering corpus in insurance domain.
• LES-MMRC: 莱斯杯：全国第二届“军事智能机器阅读”挑战赛 (方案)
• LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning.
• MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge.
• MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text.
• MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.
• NarrativeQA: The NarrativeQA Reading Comprehension Challenge.
• NewsQA: A Machine Comprehension Dataset.
• NumNet+ & NumNet+ v2: Machine Reading Comprehension with Numerical Reasoning.
• OpenBookQA: A New Dataset for Open Book Question Answering.
• QAConv: Question Answering on Informative Conversations.
• QASC: Question Answering via Sentence Composition.
• ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning.
• SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine.
• SQuAD: The Stanford Question Answering Dataset (v1.1 & v2.0).
• StreamingQA(paper): A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models.
• TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

All about Question, Answer, Evidence...

• Question-Generation-Paper-List
  • link: https://github.com/teacherpeterpan/Question-Generation-Paper-List
  • author: Liangming Pan
  • note: a summary of must-read papers for Neural Question Generation (NQG).
• Questgen.ai
  • link: https://github.com/ramsrigouthamg/Questgen.ai
  • author: Ramsri Goutham Golla
  • note: question generation using state-of-the-art nlp algorithms.
• Question-Generation
  • link: https://github.com/KristiyanVachev/Question-Generation
  • author: Kristiyan Vachev
  • note: generating multiple choice questions from text using Machine Learning..
• question_generator
  • link: https://github.com/AMontgomerie/question_generator
  • author: Adam Montgomerie
  • note: an nlp system for generating reading comprehension questions.