Fetching the paper…
Reading the bibliography…
Many extractive question answering models are trained to predict start and end positions of answers.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
Earlier work this paper cites.
Metacost: a general method for making classifiers cost-sensitive
Pedro Domingos. 1999 · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton. 2002 · 2002
Earlier work this paper cites.
The class imbalance problem: A systematic study
Nathalie Japkowicz and Shaju Stephen. 2002 · 2002
Earlier work this paper cites.
On multi-class cost-sensitive learning
Zhi-Hua Zhou and Xu-Ying Liu. 2006 · 2006
Earlier work this paper cites.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Earlier work this paper cites.
Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Earlier work this paper cites.
A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning. 2016 · 2016
Earlier work this paper cites.
Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang. 2016 · 2016
Earlier work this paper cites.
Learning recurrent span representations for extractive question answering
Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, and Jonathan Berant. 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.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang. 2016 · 2016
Earlier work this paper cites.
Yin and yang: Balancing and answering binary visual questions
Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2016 · 2016
Earlier work this paper cites.
Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
Earlier work this paper cites.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 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.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
Cited alongside, same era.
Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Cited alongside, same era.
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.
On adversarial removal of hypothesis-only bias in natural language inference
Yonatan Belinkov, Adam Poliak, Stuart M Shieber, Benjamin Van Durme, and Alexander Rush. 2019 · 2019
Later among the works it cites.
Understanding dataset design choices for multi-hop reasoning
Jifan Chen and Greg Durrett. 2019 · 2019
Later among the works it cites.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Caiming Xiong, Victor Zhong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 2018 · 2018
Cited alongside, same era.
Adventure: Adversarial training for textual entailment with knowledge-guided examples
Dongyeop Kang, Tushar Khot, Ashish Sabharwal, and Eduard Hovy. 2018 · 2018
Cited alongside, same era.
Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018 · 2018
Cited alongside, same era.
Adversarially regularising neural nli models to integrate logical background knowledge
Pasquale Minervini and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Mrqa 2019 shared task: Evaluating generalization in reading comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
Later among the works it cites.
Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
Later among the works it cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Later among the works it cites.
Generative question answering: Learning to answer the whole question
M Lewis and A Fan. 2019 · 2019
Later among the works it cites.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
Later among the works it cites.
Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 2019
Later among the works it cites.
Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
Later among the works it cites.
Real-time open-domain question answering with dense-sparse phrase index
Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, and Hannaneh Hajishirzi. 2019 · 2019
Later among the works it cites.
Multiqa: An empirical investigation of generalization and transfer in reading comprehension
Alon Talmor and Jonathan Berant. 2019 · 2019
Later among the works it cites.
End-to-end bias mitigation by modelling biases in corpora
Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson. 2020 · 2020
Closest in time.