- Minghao Hu, Yuxing Peng, Zhen Huang and Dongsheng Li, A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning, link.
- Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao and Hongning Wang, Adversarial Domain Adaptation for Machine Reading Comprehension, link?.
- Yimin Jing, Deyi Xiong and Zhen Yan, BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels, link?.
- Lifu Huang, Ronan Le Bras, Chandra Bhagavatula and Yejin Choi, Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning, link?.
- Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang and Guoping Hu, Cross-Lingual Machine Reading Comprehension, link?.
- Todor Mihaylov and Anette Frank, Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension, link?.
- Kyungjae Lee, Sunghyun Park, Hojae Han, Jinyoung Yeo, Seung-won Hwang and Juho Lee, Learning with Limited Data for Multilingual Reading Comprehension, link?.
- Qiu Ran, Yankai Lin, Peng Li, Jie Zhou and Zhiyuan Liu, NumNet: Machine Reading Comprehension with Numerical Reasoning, link?.
- 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, link?.
- Daniel Andor, Luheng He, Kenton Lee and Emily Pitler, Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension, link?.
- Delai Qiu, Yuanzhe Zhang, Xinwei Feng, Xiangwen Liao, Wenbin Jiang, Yajuan Lyu, Kang Liu and Jun Zhao, Machine Reading Comprehension Using Structural Knowledge Graph-aware Network, link?.
- Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, Noah A. Smith and Matt Gardner, Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning, link?.
- 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, link?.
- Yimeng Zhuang and Huadong Wang, Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension, link.
- Chao Wang and Hui Jiang, Explicit Utilization of General Knowledge in Machine Reading Comprehension, link.
- Kyosuke Nishida et al., Multi-style Generative Reading Comprehension, link.
- Minghao Hu, Yuxing Peng, Zhen Huang and Dongsheng Li, Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension, link.
- Ming Ding et al., Cognitive Graph for Multi-Hop Reading Comprehension at Scale, link.
- Ming Tu et al., Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs, link.
- An Yang et al., Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension, link.
- Alon Talmor and Jonathan Berant, MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension, link.
- Yichen Jiang el at., Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension, link.
- Yi Tay et al., Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives, link.
- Sewon Min et al., Multi-hop Reading Comprehension through Question Decomposition and Rescoring, link.
- Souvik Kundu et al., Exploiting Explicit Paths for Multi-hop Reading Comprehension, link.
- Haichao Zhu et al., Learning to Ask Unanswerable Questions for Machine Reading Comprehension, link.
- Xuanyu Zhang, MC^2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension, link.
- Diana Galvan, Active Reading Comprehension: A dataset for learning the Question-Answer Relationship strategy, link.
- Danqi Chen: Neural Reading Comprehension and Beyond. PhD thesis, Stanford University, 2018, link.
- Boyu Qiu et al., A Survey on Neural Machine Reading Comprehension, arXiv, 2019, link.
- Xin Zhang et al., Machine Reading Comprehension: a Literature Review, arXiv, 2019, link.
- Shanshan Liu et al., Neural Machine Reading Comprehension: Methods and Trends, arXiv, 2019, link.
Noted
- KBMRC: Knowledge-based Machine Reading Comprehension
- OPQA: Open-domain Question Answering
Year | Title | Model | Conference | Author/Team | Datasets | Misc | Paper, Source Code |
---|---|---|---|---|---|---|---|
2015 | Teaching Machines to Read and Comprehend | NIPS | Google DeepMind, University of Oxford | CNN and Daily Mail | paper, code | ||
2016 | Text Understanding with the Attention Sum Reader Network | ACL | IBM Watson | Children’s Book Test, CNN and Daily Mail | paper, code | ||
2016 | Key-Value Memory Networks for Directly Reading Documents | EMNLP | Facebook AI Research, CMU | WikiMovies, WikiQA | KBMRC | paper, code | |
2017 | Bi-directional Attention Flow for Machine Comprehension | BiDAF | ICLR | University of Washington, Allen Institute | SQuAD 1.1 | paper, code | |
2017 | Machine Comprehension Using Match-LSTM and Answer Pointer | ICLR | Singapore Management University | SQuAD 1.1 | paper, code | ||
2017 | R-Net: Machine Reading Comprehension with Self-Matching Networks | R-Net | Technical Report | Microsoft Research Asia | SQuAD 1.1, MS-MARCO | paper, code | |
2017 | Reading Wikipedia to Answer Open-Domain Questions | DrQA | ACL | Danqi Chen et al., | SQuAD 1.1, CuratedTREC, WebQuestions, WikiMovies | OPQA | paper, code |
2017 | Gated-Attention Readers for Text Comprehension | ACL | CMU | CNN and Daily Mail, Children’s Book Test, Who Did What | paper, code | ||
2017 | Leveraging Knowledge Bases in LSTMs for Improving Machine Reading | ACL | CMU | KBMRC | paper | ||
2017 | World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions | EMNLP | McGill University | KBMRC | paper | ||
2018 | QANET: Combining local Convolution with global Self-Attention for Reading Comprehension | QANet | ICLR | CMU, Google Brain | SQuAD 1.1 | paper, code | |
2018 | Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge | ACL | Heidelberg University | KBMRC | paper | ||
2019 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | BERT | NAACL | Google AI Language | GLUE, SQuAD 1.1, SQuAD 2.0, SWAG | paper, code | |
2019 | Knowledge Based Machine Reading Comprehension | Harbin Institute, MRA, ... | KBMRC | paper | |||
2019 | Explicit Utilization of General Knowledge in Machine Reading Comprehension | York University | KBMRC | paper | |||
paper |
- Following Danqi Chen, we have four answer types: Cloze test, Multiple choice, Span extraction and Free answering.
Year | Dataset | Task | Size | Source | Web/Paper | Answer type | Misc | Similar datasets |
---|---|---|---|---|---|---|---|---|
1999 | DeepRead | RC | 60 development and 60 test? | news stories | paper | Free answering | ||
2013 | QA4MRE | RC | various articles | paper | Multiple choice | |||
2013 | MCTest | RC | 500 stories + 2k questions | fictional stories | paper | Multiple choice | open-domain | |
2015 | CNN and Daily Mail | RC | 93k + 220k articles | CNN + Daily Mail | paper | Cloze test | ||
2015 | bAbI | RC | classic text adventure game | web | Free answering | 20 tasks | ||
2015 | Children's Book Test | RC | 108 children's books | web | Cloze test | |||
2016 | LAMBADA | paper | Cloze test | |||||
2016 | Who-did-What | paper | Cloze test | |||||
2016 | MS MARCO | web | Free answering | |||||
2016 | NewsQA | paper | Span extraction | |||||
2016 | SQuAD 1.1 | RC | Wikipedia | paper | Span extraction | NewsQA | ||
2017 | TriviaQA | paper | Span extraction | |||||
2017 | RACE | paper | Multiple choice | |||||
2017 | SQA | paper | ||||||
2017 | NarrativeQA | paper | Free answering | |||||
2017 | SearchQA | paper | Free answering | |||||
2017 | WikiSuggest | paper | ||||||
2018 | MultiRC | RC | 6k+ questions | various articles | web, paper | Multiple choice | multiple sentence reasoning | MCTest |
2018 | CSQA | QA | 200k dialogs, 1.6M turns | paper | ||||
2018 | CQA | paper | ||||||
2018 | SQuAD 2.0 | RC | 150k | Wikipedia | paper | Span extraction | no answer: 50k | NewsQA |
2018 | CoQA | RC | 127k | various articles | paper | Free answering | conversational questions | QuAC |
2018 | Quac | RC | 100k | Wikipedia | paper | Span extraction | conversational questions | CoQA |
2018 | HotpotQA | RC | 113k | Wikipedia | web, paper | Span extraction | multi-hop reasoning | QAngaroo |
2018 | SWAG | QA | 113k | video caption | Multiple choice | situational commonsense reasoning | ||
2018 | OpenBookQA | QA | 6k | science facts | Multiple choice | external knowledge | ARC | |
2018 | RecipeQA | RC | 36k | various | multimodal comprehension | |||
2018 | CLOTH | RC | 99k | English exams | Cloze test | RACE | ||
2018 | DuoRC | RC | 186k | movie plot | Span extraction | NarrativeQA | ||
2018 | CliCR | RC | 100k | clinical case text | Cloze test | |||
2018 | FEVER | |||||||
2018 | ProPara | RC | 2k | procedural text | bAbI, SCoNE | |||
2018 | ARC | RC | 8k | science exam | easy 5197, challenge 2590 | |||
2018 | DuReader | Free answering |