Fetching the paper…
Reading the bibliography…
In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly.
Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome T. Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. 2019 · 1901
Earlier work this paper cites.
Harvesting paragraph-level question-answer pairs from Wikipedia
Xinya Du and Claire Cardie. 2018 · 1917
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
Earlier work this paper cites.
Accelerating the annotation of sparse named entities by dynamic sentence selection
Yoshimasa Tsuruoka, Jun’ichi Tsujii, and Sophia Ananiadou. 2008 · 2008
Earlier work this paper cites.
Active learning literature survey
Burr Settles. 2009 · 2009
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.
Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Earlier work this paper cites.
Towards linguistically generalizable NLP systems: A workshop and shared task
Allyson Ettinger, Sudha Rao, Hal Daumé III, and Emily M. Bender. 2017 · 2017
Earlier work this paper cites.
The effect of different writing tasks on linguistic style: A case study of the ROC story cloze task
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017 · 2017
Earlier work this paper cites.
Making neural QA as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017 · 2017
Earlier work this paper cites.
Mastering the dungeon: Grounded language learning by mechanical turker descent
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H Miller, Arthur Szlam, Douwe Kiela, and Jason Weston. 2017 · 2017
Earlier work this paper cites.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Earlier work this paper cites.
Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018 · 2018
Earlier work this paper cites.
Synthetic QA corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 2019
Cited alongside, same era.
Build it break it fix it for dialogue safety: Robustness from adversarial human attack
Emily Dinan, Samuel Humeau, Bharath Chintagunta, and Jason Weston. 2019 · 2019
Cited alongside, same era.
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
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.
Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019 · 2019
Cited alongside, same era.
Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020 · 2020
Later among the works it cites.
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021 · 2021
Closest in time.
What will it take to fix benchmarking in natural language understanding?
Samuel R Bowman and George E Dahl. 2021 · 2021
Closest in time.
On the efficacy of adversarial data collection for question answering: Results from a large-scale randomized study
Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton, and Wen-tau Yih. 2021 · 2021
Closest in time.
Dynabench: Rethinking benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative question answering: Learning to answer the whole question
Mike Lewis and Angela Fan. 2019 · 2019
Cited alongside, same era.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
Cited alongside, same era.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020 · 2020
Cited alongside, same era.
New protocols and negative results for textual entailment data collection
Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, and Emily Pitler. 2020 · 2020
Cited alongside, same era.
Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Closest in time.
PAQ: 65 million probably-asked questions and what you can do with them
Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. 2021 · 2021
Closest in time.
KILT: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021 · 2021
Closest in time.
Adversarially constructed evaluation sets are more challenging, but may not be fair
Jason Phang, Angelica Chen, William Huang, and Samuel R Bowman. 2021 · 2021
Closest in time.
DynaSent: A dynamic benchmark for sentiment analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2021 · 2021
Closest in time.
Tailor: Generating and perturbing text with semantic controls
Alexis Ross, Tongshuang Wu, Hao Peng, Matthew E. Peters, and Matt Gardner. 2021 · 2021
Closest in time.
Comparing test sets with item response theory
Clara Vania, Phu Mon Htut, William Huang, Dhara Mungra, Richard Yuanzhe Pang, Jason Phang, Haokun Liu, Kyunghyun Cho, and Samuel R. Bowman. 2021 · 2021
Closest in time.
Analyzing dynamic adversarial training data in the limit
Eric Wallace, Adina Williams, Robin Jia, and Douwe Kiela. 2021 · 2021
Closest in time.
Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2021 · 2021
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.