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https://github.com/ZhengZixiang/MRCPapers

Worth-reading paper list and other awesome resources on Machine Reading Comprehension (MRC) and textual Question Answering (QA). 机器阅读理解与文本问答领域值得一读的论文列表和其他相关资源集合
https://github.com/ZhengZixiang/MRCPapers

List: MRCPapers

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Worth-reading paper list and other awesome resources on Machine Reading Comprehension (MRC) and textual Question Answering (QA). 机器阅读理解与文本问答领域值得一读的论文列表和其他相关资源集合

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# MRCPapers

Worth-reading paper list and other awesome resources on Machine Reading Comprehension (MRC) and Question Answering (QA). Suggestions about adding papers, repositories and other resource are welcomed!



机器阅读理解领域值得一读的论文列表和其他相关资源集合。欢迎新增论文、代码仓库与其他资源等建议！



## Paper

- **Bidirectional Attention Flow for Machine Comprehension**. *Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi*. (ICLR 2017) [[paper]](https://openreview.net/forum?id=HJ0UKP9ge)[[code]](https://allenai.github.io/bi-att-flow/) - ***BiDAF***

- **Read + Verify: Machine Reading Comprehension with Unanswerable Questions**. *Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li*. (AAAI-2019) [[paper]](https://arxiv.org/pdf/1808.05759.pdf)[[unofficial code]](https://github.com/woshiyyya/Answer-Verifier-pytorch) - ***Verfier***

- **Cognitive Graph for Multi-Hop Reading Comprehension at Scale**. *Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang*. (ACL 2019) [[paper]](https://arxiv.org/abs/1905.05460) - ***CogQA***

- **Dual Co-Matching Network for Multi-choice Reading Comprehension**. *Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou*. (CoRR 2019) [[paper]](https://arxiv.org/abs/1901.09381) - ***DCMN***

- **A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning**. *Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li*. (EMNLP 2019) [[paper]](https://arxiv.org/abs/1908.05514) - ***MTMSN***

- **Tag-based Multi-Span Extraction in Reading Comprehension**. *Avia Efrat, Elad Segal, Mor Shoham*. (CoRR 2019) [[paper]](https://arxiv.org/abs/1909.13375)

- **SG-Net: Syntax-Guided Machine Reading Comprehension**. *Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang*. (AAAI 2020) [[paper]](https://arxiv.org/abs/1908.05147)[[code]](https://github.com/cooelf/SG-Net) - ***SG-Net***

- **DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension**. *Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou*. (AAAI 2020) [[paper]](https://arxiv.org/abs/1908.11511.pdf)

- **MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension**. *Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, Dilek Hakkani-tur*. (AAAI 2020) [[paper]](https://arxiv.org/abs/1910.00458)[[code]](https://github.com/jind11/MMM-MCQA)

- **Retrospective Reader for Machine Reading Comprehension**. *Zhuosheng Zhang, Junjie Yang, Hai Zhao*. (CoRR 2020) [[paper]](https://arxiv.org/abs/2001.09694)[[Chinese blog]](https://mp.weixin.qq.com/s?__biz=MzIwMTc4ODE0Mw==&mid=2247502891&idx=1&sn=8f3d552ee384544d0b9a868e01b91ed9&key=5fa67e91c99877c949e72c80560ca0bb5dc99de132236c4b530784a3c3c2cc93a94dcd482b4968b128c7bc7553888c5df30cc4f734abb1a63a2bd02402645a9b966bd4291e333ef13e861eb06c80822a&ascene=1&uin=Mjg1NTM0NDcyMw%3D%3D&devicetype=Windows+10&version=6208006f&lang=zh_CN&exportkey=A%2BCwv01k%2FtyMn%2Ft38iF3KbY%3D&pass_ticket=nkIz09BYlgtIrHo7XkM4ahTkS8sck64jbLwU0LotdcTnxt2f%2FIuSGmn33Pc7gW1f) - ***Retro-Reader***

- **DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering**. *Qingqing Cao, Harsh Trivedi, Aruna Balasubramanian, Niranjan Balasubramanian*. (ACL 2020) [[paper]](https://arxiv.org/abs/2005.00697)[[code]](https://github.com/StonyBrookNLP/deformer) - ***DeFormer***

- **Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension**. *Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, Dong Yu*. (ACL 2020) [[paper]](https://arxiv.org/abs/2005.08056)[[code]](https://github.com/HongyuGong/RCM-Question-Answering)

- **Is Graph Structure Necessary for Multi-hop Question Answering?**. *Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu*. (EMNLP 2020) [[paper]](https://arxiv.org/abs/2004.03096)

- **DUMA: Reading Comprehension with Transposition Thinking**. *Pengfei Zhu, Hai Zhao, Xiaoguang Li*. (CoRR 2020) [[paper]](https://arxiv.org/abs/2001.09415)

- **No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension**. *Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, Daxin Jiang*. (Findings of EMNLP 2020) [[paper]](https://arxiv.org/abs/2009.12056)

- **CogLTX: Applying BERT to Long Texts**. *Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang*. (NeurIPS 2020) [[paper]](https://proceedings.neurips.cc/paper/2020/hash/96671501524948bc3937b4b30d0e57b9-Abstract.html)[[code]](https://github.com/Sleepychord/CogLTX)

- **HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions**. *Shaobo Li, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Chengjie Sun, Zhenzhou Ji, Bingquan Liu*. (AAAI 2021) [[paper]](https://arxiv.org/abs/2012.15534)[[Chinese blog]](https://mp.weixin.qq.com/s/vgx4JBJHxigySffIXXymtg)



## Survey & Review & Tutorial

- **Neural Machine Reading Comprehension: Methods and Trends**. *Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang* (Applied Sciences 2019) [[paper]](https://arxiv.org/abs/1907.01118)

- **Research on Machine Reading Comprehension and Textual Question Answering**. *Minghao Hu*. (PhD thesis 2019 in Chinese) [[paper]](https://github.com/huminghao16/thesis)

- **Neural Reading Comprehension and Beyond**. *Danqi Chen*. (PhD thesis 2019) [[paper]](https://purl.stanford.edu/gd576xb1833)

- **Open-Domain Question Answering**. *Danqi Chen*. (ACL 2020) [[slides]](https://github.com/danqi/acl2020-openqa-tutorial)

- **English Machine Reading Comprehension Datasets: A Survey**. *Daria Dzendzik, Carl Vogel, Jennifer Foster*. (CoRR 2021) [[paper]](https://arxiv.org/abs/2101.10421)



## Dataset

- **ChID: A Large-scale Chinese IDiom Dataset for Cloze Test**. *Chujie Zheng, Minlie Huang, Aixin Sun*. (ACL 2019) [[paper]](https://arxiv.org/abs/1906.01265)

- **Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension**. *Kai Sun, Dian Yu, Dong Yu, Claire Cardie*. (TACL 2020) [[paper]](https://arxiv.org/abs/1904.09679)[[data]](https://github.com/nlpdata/c3) - ***C3***

- **CoQA: A Conversational Question Answering Challenge**. *Siva Reddy, Danqi Chen, Christopher D. Manning*. (NAACL 2019 & TACL 2019) [[paper]](https://arxiv.org/pdf/1808.07042.pdf) [[data]](https://stanfordnlp.github.io/coqa/)

- **DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension**. *Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, Claire Cardie*. (TACL Vol.7 2019) [[paper]](https://arxiv.org/abs/1902.00164)[[data]](https://dataset.org/dream/)

- **DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**. *Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner*. (NAACL-HLT 2019) [[paper]](https://arxiv.org/abs/1903.00161)

- **DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications**. *Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, Haifeng Wang*. (ACL 2018 QA workshop) [[paper]](https://www.aclweb.org/anthology/W18-2605/)[[data]](https://github.com/baidu/DuReader)

- **HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**. *Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, Christopher D. Manning*. (EMNLP 2018) [[paper]](https://arxiv.org/abs/1809.09600)[[data]](https://hotpotqa.github.io/)

- **A Question-Entailment Approach to Question Answering**. *Asma Ben Abacha, Dina Demner-Fushman*. (BMC Bioinformatics 20, 511 (2019)) [[paper]](https://arxiv.org/abs/1901.08079)[[data]](https://github.com/abachaa/MedQuAD) - ***MedQuAD***

- **MIMICS: A Large-Scale Data Collection for Search Clarification**. *Hamed Zamani, Gord Lueck, Everest Chen, Rodolfo Quispe, Flint Luu, Nick Craswell*. (CIKM 2020) [[paper]](https://arxiv.org/abs/2006.10174)[[data]](https://github.com/microsoft/MIMICS)

- **Natural Questions: a Benchmark for Question Answering Research**. *Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov*. (TACL Vol.7 2019) [[paper]](https://transacl.org/ojs/index.php/tacl/article/view/1455)[[data]](https://ai.google.com/research/NaturalQuestions)

- **ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning**. *Michael Boratko, Xiang Lorraine Li, Rajarshi Das, Tim O'Gorman, Dan Le, Andrew McCallum*. (EMNLP 2020) [[paper]](https://arxiv.org/abs/2005.00771)[[data]](https://github.com/iesl/protoqa-data)

- **QuAC : Question Answering in Context**. *Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer*. (EMNLP 2018) [[paper]](https://arxiv.org/abs/1808.07036)[[data]](https://quac.ai/)

- **RACE: Large-scale ReAding Comprehension Dataset From Examinations**. *Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, Eduard Hovy*. (EMNLP 2017) [[paper]](https://arxiv.org/abs/1704.04683)[[data]](https://www.cs.cmu.edu/~glai1/data/race/)

- **SQuAD: 100,000+ Questions for Machine Comprehension of Text**. *Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang*. (EMNLP 2016) [[paper]](https://www.aclweb.org/anthology/D16-1264/)[[data]](https://github.com/rajpurkar/SQuAD-explorer/tree/master/dataset)

- **Know What You Don't Know: Unanswerable Questions for SQuAD**. *Pranav Rajpurkar, Robin Jia, Percy Liang*. (ACL 2018) [[paper]](https://www.aclweb.org/anthology/P18-2124/)[[data]](https://github.com/rajpurkar/SQuAD-explorer/tree/master/dataset) - ***SQuAD 2.0***

- **SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference**. *Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi*. (EMNLP 2018) [[paper]](https://arxiv.org/abs/1808.05326)[[data]](https://github.com/rowanz/swagaf)

- **TYDI QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages**. *Jonathan H. Clark, Jennimaria Palomaki, Vitaly Nikolaev, Eunsol Choi, Dan Garrette, Michael Collins, Tom Kwiatkowski*. (TACL 2020) [[paper]](https://storage.cloud.google.com/tydiqa/tydiqa.pdf)[[data]](https://github.com/google-research-datasets/tydiqa)



## Repository & Toolkit

- [AllenAI / allennlp-reading-comprehension](https://github.com/allenai/allennlp-reading-comprehension) - Reading comprehension tookit by the AllenNLP team

- [AMontgomerie / question_generator](https://github.com/AMontgomerie/question_generator) - An NLP system for generating reading comprehension questions

- [ankit-ai / BertQA-Attention-on-Steroids](https://github.com/ankit-ai/BertQA-Attention-on-Steroids)

- [alsang / BiDAF-pytorch](https://github.com/galsang/BiDAF-pytorch)

- [BshoterJ / awesome-kgqa](https://github.com/BshoterJ/awesome-kgqa) - A collection of some materials of knowledge graph question answering

- [cdqa-suite / cdQA](https://github.com/cdqa-suite/cdQA)

- [cdqa-suite / cdQA-annotator](https://github.com/cdqa-suite/cdQA-annotator)

- [deepset-ai / Haystack](https://github.com/deepset-ai/haystack)

- [Facebook Research / ParlAI](https://github.com/facebookresearch/ParlAI) - A framework for training and evaluating AI models on a variety of openly available dialogue datasets

- [efficientqa/retrieval-based-baselines](https://github.com/efficientqa/retrieval-based-baselines) - Tutorials on training and testing retrieval-based models

- [IndexFziQ / KMRC-Papers](https://github.com/IndexFziQ/KMRC-Papers)

- [krystalan / Multi-hopRC](https://github.com/krystalan/Multi-hopRC) - 多跳阅读理解相关论文

- [lcdevelop / ChatBotCourse](https://github.com/lcdevelop/ChatBotCourse) - 自己动手做聊天机器人教程

- [lixinsu / RCZoo](https://github.com/lixinsu/RCZoo)

- [seriousran / awesome-qa](https://github.com/seriousran/awesome-qa)

- [Sogou / SMRCToolkit](https://github.com/sogou/SMRCToolkit) - Sogou Machine Reading Comprehension (SMRC) toolkit based on TensorFlow

- [songyingxin / BERT-MRC-RACE](https://github.com/songyingxin/BERT-MRC-RACE)

- [tangbinh / question-answering](https://github.com/tangbinh/question-answering)

- [THUNLP / RCPapers](https://github.com/thunlp/RCPapers)

- [wavewangyue / kbqa](https://github.com/wavewangyue/kbqa) - 基于知识库的问答：seq2seq模型实践

- [xanhho / Reading-Comprehension-Question-Answering-Papers](https://github.com/xanhho/Reading-Comprehension-Question-Answering-Papers) - Survey on Machine Reading Comprehension

- [YingZiqiang / PyTorch-MRCToolkit](https://github.com/YingZiqiang/PyTorch-MRCToolkit)

- [yizhen20133868 / NLP-Conferences-Code](https://github.com/yizhen20133868/NLP-Conferences-Code)

- [ymcui / Chinese-RC-Datasets](https://github.com/ymcui/Chinese-RC-Datasets)

- [zcgzcgzcg1 / MRC_book](https://github.com/zcgzcgzcg1/MRC_book) - 《机器阅读理解：算法与实践》代码



## Blog Post

### English

- [Google / Progress and Challenges in Long-Form Open-Domain Question Answering](https://ai.googleblog.com/2021/03/progress-and-challenges-in-long-form.html)

- [Stanford / Beyond Local Pattern Matching: Recent Advances in Machine Reading](https://ai.stanford.edu/blog/beyond-local-pattern-matching/)

### Chinese

- [changreal / 【总结向】MRC 经典模型与技术](https://blog.csdn.net/changreal/article/details/105074629)

- [changreal / 【总结向】从CMRC2019头部排名看中文MRC](https://blog.csdn.net/changreal/article/details/105363937)

- [humdingers / 2020法研杯阅读理解赛道第一名方案](https://github.com/humdingers/2020CAIL_LDLJ)

- [Liberty / 文本太长，Transformer用不了怎么办](https://zhuanlan.zhihu.com/p/259835450)

- [Luke / 机器阅读理解之多答案抽取](https://zhuanlan.zhihu.com/p/101248202)

- [xhsun1997 / 详细介绍有关RACE数据集上经典的机器阅读理解模型](https://blog.csdn.net/m0_45478865/article/details/106869172?spm=1001.2014.3001.5501)

- [多多笔记 / 开放领域问答梳理系列(1)](https://mp.weixin.qq.com/s/iLE0zwhzd3ffri8VH24Z9g)

- [多多笔记 / 开放领域问答梳理系列(2)](https://mp.weixin.qq.com/s/6DpC1uJqrsyl0a62uLl4zw)

- [科学空间 / 学会提问的BERT：端到端地从篇章中构建问答对](https://kexue.fm/archives/7630)

- [科学空间 / “非自回归”也不差：基于MLM的阅读理解问答](https://kexue.fm/archives/7148)

- [科学空间 / 万能的seq2seq：基于seq2seq的阅读理解问答](https://kexue.fm/archives/7115)

- [平安寿险PAI / 机器阅读理解探索与实践](https://zhuanlan.zhihu.com/p/109309164)

- [知乎问答 / 机器阅读理解方向有什么值得follow的大佬，网站等等](https://www.zhihu.com/question/358469127/answer/1028144909)

- [知乎问答 / Bert 如何解决长文本问题？](https://www.zhihu.com/question/327450789)