xanhho / Reading Comprehension Question Answering Papers

Content

• Survey papers
• Slides
• Evaluation papers
• Models (for single-hop)
• Knowledge-based Machine Reading Comprehension
• Open-domain Question Answering
• Unanswerable Questions
• Multi-Passage Machine Reading Comprehension
• Conversational Question Answering
• Datasets
• Datasets with Explanations
• QA over KG
• Question answering systems
• Knowledge bases/Knowledge sources
• Papers misc

Survey/Overview papers/documents should read on Machine Reading Comprehension

• Fengbin Zhu et al., Retrieving and Reading : A Comprehensive Survey on Open-domain Question Answering, arXiv, 2021, paper
• Mokanarangan Thayaparan, Marco Valentino, and André Freitas, A Survey on Explainability in Machine Reading Comprehension, arXiv, 2020, paper
• Viktor Schlegel et al., Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models, arXiv, 2020, paper
• Viktor Schlegel et al., A Framework for Evaluation of Machine Reading Comprehension Gold Standards, arXiv, 2020, paper
• Chengchang Zeng et al., A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets, arXiv, 2020, paper.
• Razieh Baradaran, Razieh Ghiasi, and Hossein Amirkhani, A Survey on Machine Reading Comprehension Systems, arXiv, 6 Jan 2020, paper.
• Matthew Gardner et al., On Making Reading Comprehension More Comprehensive., aclweb, 2019, paper.
• Shanshan Liu et al., Neural Machine Reading Comprehension: Methods and Trends, arXiv, 2019, paper.
• Xin Zhang et al., Machine Reading Comprehension: a Literature Review, arXiv, 2019, paper.
• Boyu Qiu et al., A Survey on Neural Machine Reading Comprehension, arXiv, 2019, paper.
• Danqi Chen: Neural Reading Comprehension and Beyond. PhD thesis, Stanford University, 2018, paper.

Slides

• Sebastian Riedel, Reading and Reasoning with Neural Program Interpreters, slides, MRQA 2018.
• Phil Blunsom, Data driven reading comprehension: successes and limitations, slides, MRQA 2018.
• Jianfeng Gao, Multi-step reasoning neural networks for question answering, slides, MRQA 2018.
• Sameer Singh, Questioning Question Answering Answers, slides, MRQA 2018.

Evaluation papers

• Diana Galvan, Active Reading Comprehension: A dataset for learning the Question-Answer Relationship strategy, ACL 2019, paper.
• Divyansh Kaushik and Zachary C. Lipton, How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks, EMNLP 2018, paper.
• Saku Sugawara et al., What Makes Reading Comprehension Questions Easier?, EMNLP 2018, paper.
• Pramod K. Mudrakarta et al., Did the Model Understand the Question?, ACL 2018, paper.
• Robin Jia and Percy Liang, Adversarial Examples for Evaluating Reading Comprehension Systems, EMNLP 2017, paper.
• Saku Sugawara et al., Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability, ACL 2017, paper.
• Saku Sugawara et al., Prerequisite Skills for Reading Comprehension: Multi-perspective Analysis of MCTest Datasets and Systems, AAAI 2017, paper.
• Danqi Chen et al., A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task, ACL 2016, paper.

Basic Papers/Models

KBMRC: Knowledge-based Machine Reading Comprehension

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/Knowledge-based-MRC

OPQA: Open-domain Question Answering

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/Open-domain-Question-Answering

UQ: Unanswerable Questions

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/Unanswerable-Questions

Multi-Passage MRC: Multi-Passage Machine Reading Comprehension

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/Multi-Passage-MRC

CQA: Conversational Question Answering

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/CQA

Datasets

• Following Danqi Chen, we have four answer types:

  • Cloze test
  • Multiple choice
  • Span extraction
  • Free answering

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/MRC-Datasets

Datasets with Explanations

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/Datasets-with-Explanations

QA over KG

• Details: https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers/wiki/QA-over-KG

Knowledge Bases/Knowledge Sources

• Wikidata, web
• Freebase, paper
• DBPedia

Question Answering Systems

• IBM's DeepQA
• QuASE
• Microsoft's AskMSR
• YodaQA
• DrQA

Others (Misc: Model, transfer learning, data augmentation, domain adaption, cross lingual ...)

• Minghao Hu, Yuxing Peng, Zhen Huang and Dongsheng Li, A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning, EMNLP 2019, paper.
• Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao and Hongning Wang, Adversarial Domain Adaptation for Machine Reading Comprehension, EMNLP 2019, paper.
• Yimin Jing, Deyi Xiong and Zhen Yan, BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels, EMNLP 2019, paper.
• Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang and Guoping Hu, Cross-Lingual Machine Reading Comprehension, EMNLP 2019, paper.
• Todor Mihaylov and Anette Frank, Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension, EMNLP 2019, paper.
• Kyungjae Lee, Sunghyun Park, Hojae Han, Jinyoung Yeo, Seung-won Hwang and Juho Lee, Learning with Limited Data for Multilingual Reading Comprehension, EMNLP 2019, paper.
• Qiu Ran, Yankai Lin, Peng Li, Jie Zhou and Zhiyuan Liu, NumNet: Machine Reading Comprehension with Numerical Reasoning, EMNLP 2019, paper.
• Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang and Guoping Hu, A Span-Extraction Dataset for Chinese Machine Reading Comprehension, EMNLP 2019, paper.
• Daniel Andor, Luheng He, Kenton Lee and Emily Pitler, Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension, EMNLP 2019, paper.
• Tsung-Yuan Hsu, Chi-Liang Liu and Hung-yi Lee, Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model, EMNLP 2019, paper.
• Kyosuke Nishida et al., Multi-style Generative Reading Comprehension, ACL 2019, paper.
• Alon Talmor and Jonathan Berant, MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension, ACL 2019, paper.
• Yi Tay et al., Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives, ACL 2019, paper.
• Haichao Zhu et al., Learning to Ask Unanswerable Questions for Machine Reading Comprehension, ACL 2019, paper.
• Patrick Lewis et al., Unsupervised Question Answering by Cloze Translation, ACL 2019, paper.
• Michael Hahn and Frank Keller, Modeling Human Reading with Neural Attention, EMNLP 2016, paper.
• Jianpeng Cheng et al., Long Short-Term Memory-Networks for Machine Reading, EMNLP 2016, paper.

Thanks to these repositories:

• https://github.com/penzant/nlu_datasets_2018
• https://github.com/seriousmac/awesome-qa