Fetching the paper…
Reading the bibliography…
We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers.
The trec question answering track
Ellen M Voorhees. 2001 · 2001
Earlier work this paper cites.
Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
Earlier work this paper cites.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Ms marco: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Earlier work this paper cites.
Movieqa: Understanding stories in movies through question-answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2016 · 2016
Earlier work this paper cites.
Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
Earlier work this paper cites.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Earlier work this paper cites.
Visual dialog
Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José MF Moura, Devi Parikh, and Dhruv Batra. 2017 · 2017
Cited alongside, same era.
Reinforced mnemonic reader for machine reading comprehension
Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, and Ming Zhou. 2017 · 2017
Cited alongside, same era.
Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen tau Yih, and Ming-Wei Chang. 2017 · 2017
Cited alongside, same era.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
The narrativeqa reading comprehension challenge
Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2017 · 2017
Cited alongside, same era.
Tvqa: Localized, compositional video question answering
Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. 2018 · 2018
Closest in time.
Game-based video-context dialogue
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
Closest in time.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Closest in time.
Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2018 · 2018
Closest in time.
Interpretation of natural language rules in conversational machine reading
Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Signh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, and Sebastian Riedel. 2018 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gated self-matching networks for reading comprehension and question answering
Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017 · 2017
Cited alongside, same era.
Quac: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2018 · 2018
Cited alongside, same era.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Cited alongside, same era.
A dataset and baselines for sequential open-domain question answering
Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. 2018 · 2018
Cited alongside, same era.
Looking beyond the surface:a challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
Cited alongside, same era.
Closest in time.
Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph
Amrita Saha, Vardaan Pahuja, Mitesh M Khapra, Karthik Sankaranarayanan, and Sarath Chandar. 2018 · 2018
Closest in time.
The web as knowledge-base for answering complex questions
A. Talmor and J. Berant. 2018 · 2018
Closest in time.
Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering
Wei Wang, Ming Yan, and Chen Wu. 2018 · 2018
Closest in time.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Closest in time.