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We introduce a method to determine if a certain capability helps to achieve an accurate model of given data.
Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. 2019 · 1901
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Roberta: A robustly optimized BERT pretraining approach
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Modeling by shortest data description
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Present position and potential developments: Some personal views: Statistical theory: The prequential approach
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Universal coding, information, prediction, and estimation
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Occam’s razor
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Keeping the neural networks simple by minimizing the description length of the weights
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
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Item response theory: Parameter estimation techniques
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Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
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The fifth pascal recognizing textual entailment challenge
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The winograd schema challenge
Hector J. Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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Models of translation competitions
Mark Hopkins and Jonathan May. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
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VQA: Visual Question Answering
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Challenges of studying and processing dialects in social media
Anna Jørgensen, Dirk Hovy, and Anders Søgaard. 2015 · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Does string-based neural MT learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio. 2017 · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
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Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QA
Yichen Jiang and Mohit Bansal. 2019 · 2019
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Learning latent parameters without human response patterns: Item response theory with artificial crowds
John P. Lalor, Hao Wu, and Hong Yu. 2019 · 2019
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Item response theory in ai: Analysing machine learning classifiers at the instance level
Fernando Martínez-Plumed, Ricardo B.C. Prudêncio, Adolfo Martínez-Usó, and José Hernández-Orallo. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Finding generalizable evidence by learning to convince Q&A models
Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, and Kyunghyun Cho. 2019 · 2019
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. 2017 · 2017
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Social bias in elicited natural language inferences
Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017 · 2017
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Gender and dialect bias in YouTube’s automatic captions
Rachael Tatman. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
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Explain yourself! leveraging language models for commonsense reasoning
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
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Language models are few-shot learners
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Calibration of pre-trained transformers
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Adversarial NLI: A new benchmark for natural language understanding
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Unsupervised question decomposition for question answering
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Thang M. Pham, Trung Bui, Long Mai, and Anh Nguyen. 2020 · 2020
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Information-theoretic probing for linguistic structure
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Assessing the benchmarking capacity of machine reading comprehension datasets
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Information-theoretic probing with minimum description length
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Evaluating representations by the complexity of learning low-loss predictors
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasovic, and Noah A. Smith. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Bert & family eat word salad: Experiments with text understanding
Ashim Gupta, Giorgi Kvernadze, and Vivek Srikumar. 2021 · 2021
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Predicting inductive biases of pre-trained models
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