Fetching the paper…
Reading the bibliography…
Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained language models (PLMs).
Global entity disambiguation with pretrained contextualized embeddings of words and entities
Ikuya Yamada, Koki Washio, Hiroyuki Shindo, and Yuji Matsumoto. 2020 · 1909
Earlier work this paper cites.
Self-taught learning: transfer learning from unlabeled data
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y. Ng. 2007 · 2007
Earlier work this paper cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2020 · 2009
Earlier work this paper cites.
A simple and effective self-supervised contrastive learning framework for aspect detection
Tian Shi, Liuqing Li, Ping Wang, and Chandan K Reddy. 2020 · 2009
Earlier work this paper cites.
Ruijian Xu, Chongyang Tao, Daxin Jiang, Xueliang Zhao, Dongyan Zhao, and Rui Yan. 2020 · 2009
Earlier work this paper cites.
Unsupervised multi-hop question answering by question generation
Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, and William Yang Wang. 2020 · 2010
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba. 2015 · 2015
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.
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.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Earlier work this paper cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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. 2017 · 2017
Cited alongside, same era.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang. 2017 · 2017
Cited alongside, same era.
Leveraging knowledge bases in LSTMs for improving machine reading
Bishan Yang and Tom Mitchell. 2017 · 2017
Cited alongside, same era.
Simple and effective semi-supervised question answering
Bhuwan Dhingra, Danish Danish, and Dheeraj Rajagopal. 2018 · 2018
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Later among the works it cites.
Unsupervised domain adaptation on reading comprehension
Yu Cao, Meng Fang, Baosheng Yu, and Joey Tianyi Zhou. 2020 · 2020
Later among the works it cites.
Template-based question generation from retrieved sentences for improved unsupervised question answering
Alexander Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang. 2020 · 2020
Later among the works it cites.
Span selection pre-training for question answering
Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, and Avi Sil. 2020 · 2020
Later among the works it cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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
Cited alongside, same era.
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 M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Cited alongside, same era.
Unsupervised question answering by cloze translation
Patrick Lewis, Ludovic Denoyer, and Sebastian Riedel. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Self-supervised learning for contextualized extractive summarization
Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, and William Yang Wang. 2019 · 2019
Cited alongside, same era.
Self-supervised dialogue learning
Jiawei Wu, Xin Wang, and William Yang Wang. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Handling anomalies of synthetic questions in unsupervised question answering
Giwon Hong, Junmo Kang, Doyeon Lim, and Sung-Hyon Myaeng. 2020 · 2020
Later among the works it cites.
Regularization of distinct strategies for unsupervised question generation
Junmo Kang, Giwon Hong, Haritz Puerto San Roman, and Sung-Hyon Myaeng. 2020 · 2020
Later among the works it cites.
Harvesting and refining question-answer pairs for unsupervised QA
Zhongli Li, Wenhui Wang, Li Dong, Furu Wei, and Ke Xu. 2020 · 2020
Later among the works it cites.
MultiCQA: Zero-shot transfer of self-supervised text matching models on a massive scale
Andreas Rücklé, Jonas Pfeiffer, and Iryna Gurevych. 2020 · 2020
Later among the works it cites.
What do models learn from question answering datasets?
Priyanka Sen and Amir Saffari. 2020 · 2020
Later among the works it cites.
On the importance of diversity in question generation for QA
Md Arafat Sultan, Shubham Chandel, Ramón Fernandez Astudillo, and Vittorio Castelli. 2020 · 2020
Later among the works it cites.
Improving limited labeled dialogue state tracking with self-supervision
Chien-Sheng Wu, Steven C.H. Hoi, and Caiming Xiong. 2020 · 2020
Later among the works it cites.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.