Fetching the paper…
Reading the bibliography…
Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
Earlier work this paper cites.
R Thomas McCoy, Junghyun Min, and Tal Linzen. 2019a · 1911
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton. 2002 · 2002
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
Earlier work this paper cites.
Resolving complex cases of definite pronouns: the winograd schema challenge
Altaf Rahman and Vincent Ng. 2012 · 2012
Earlier work this paper cites.
A sick cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. 2014 · 2014
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.
Framenet+: Fast paraphrastic tripling of framenet
Ellie Pavlick, Travis Wolfe, Pushpendre Rastogi, Chris Callison-Burch, Mark Dredze, and Benjamin Van Durme. 2015 · 2015
Earlier work this paper cites.
Semantic proto-roles
Drew Reisinger, Rachel Rudinger, Francis Ferraro, Craig Harman, Kyle Rawlins, and Benjamin Van Durme. 2015 · 2015
Earlier work this paper cites.
Natural language inference by tree-based convolution and heuristic matching
Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. 2016 · 2016
Earlier work this paper cites.
Most” babies” are” little” and most” problems” are” huge”: Compositional entailment in adjective-nouns
Ellie Pavlick and Chris Callison-Burch. 2016 · 2016
Earlier work this paper cites.
Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
Earlier work this paper cites.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Natural language inference over interaction space
Yichen Gong, Heng Luo, and Jian Zhang. 2017 · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Natural language inference from multiple premises
Alice Lai, Yonatan Bisk, and Julia Hockenmaier. 2017 · 2017
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017 · 2017
Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
Later among the works it cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Overcoming language priors in visual question answering with adversarial regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. 2018 · 2018
Later among the works it cites.
Tackling the story ending biases in the story cloze test
Rishi Sharma, James Allen, Omid Bakhshandeh, and Nasrin Mostafazadeh. 2018 · 2018
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Later among the works it cites.
Rubi: Reducing unimodal biases in visual question answering
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian. 2017 · 2017
Cited alongside, same era.
Inference is everything: Recasting semantic resources into a unified evaluation framework
Aaron Steven White, Pushpendre Rastogi, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
Cited alongside, same era.
Ordinal common-sense inference
Sheng Zhang, Rachel Rudinger, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith. 2018 · 2018
Cited alongside, same era.
How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
Scitail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
Cited alongside, same era.
Remi Cadene, Corentin Dancette, Hedi Ben-younes, Matthieu Cord, and Devi Parikh. 2019 · 2019
Closest in time.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
Closest in time.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
Adversarial regularization for visual question answering: Strengths, shortcomings, and side effects
Gabriel Grand and Yonatan Belinkov. 2019 · 2019
Closest in time.
Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
Closest in time.
Towards debiasing fact verification models
Tal Schuster, Darsh J Shah, Yun Jie Serene Yeo, Daniel Filizzola, Enrico Santus, and Regina Barzilay. 2019 · 2019
Closest in time.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
Closest in time.