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Most recent progress in natural language understanding (NLU) has been driven, in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc.
“Unifying Question Answering, Text Classification, and Regression via Span Extraction”, 2019
Nitish Keskar, Bryan McCann, Caiming Xiong and Richard Socher · 1904
Earlier work this paper cites.
“RoBERTa: A Robustly Optimized BERT Pretraining Approach”
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer and Veselin Stoyanov · 1907
Earlier work this paper cites.
“Cross-lingual Name Tagging and Linking for 282 Languages”
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight and Heng Ji · 1958
Earlier work this paper cites.
“Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition”
Erik. Tjong and Fien De · 2003
Earlier work this paper cites.
“DeBERTa: Decoding-enhanced BERT with Disentangled Attention”, 2020
Pengcheng He, Xiaodong Liu, Jianfeng Gao and Weizhu Chen · 2006
Earlier work this paper cites.
“Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks”
Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai and Andrew McCallum · 2009
Earlier work this paper cites.
“Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher. Manning, Andrew Ng and Christopher Potts · 2013
Earlier work this paper cites.
“SQuAD: 100,000+ Questions for Machine Comprehension of Text”
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev and Percy Liang · 2016
Earlier work this paper cites.
“The Natural Language Decathlon: Multitask Learning as Question Answering”
Bryan McCann, Nitish Keskar, Caiming Xiong and Richard Socher · 2018
Earlier work this paper cites.
“Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks”
Jason Phang, Thibault Févry and Samuel Bowman · 2018
Earlier work this paper cites.
“Know What You Don’t Know: Unanswerable Questions for SQuAD”
Pranav Rajpurkar, Robin Jia and Percy Liang · 2018
Earlier work this paper cites.
“GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy and Samuel Bowman · 2018
Earlier work this paper cites.
“A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference”
Adina Williams, Nikita Nangia and Samuel Bowman · 2018
Earlier work this paper cites.
“ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension”
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh and Benjamin Durme · 2018
Earlier work this paper cites.
“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2019
Earlier work this paper cites.
“Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks”
Zi-Yi Dou, Keyi Yu and Antonios Anastasopoulos · 2019
Cited alongside, same era.
“Multi-Task Deep Neural Networks for Natural Language Understanding”
Xiaodong Liu, Pengcheng He, Weizhu Chen and Jianfeng Gao · 2019
Cited alongside, same era.
“Enhancing Deep Active Learning Using Selective Self-Training For Image Classification”, 2019
Emmeleia-Panagiota Mastoropoulou · 2019
Cited alongside, same era.
“Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark”
Nikita Nangia and Samuel. Bowman · 2019
Cited alongside, same era.
“Language Models are Unsupervised Multitask Learners”, 2019
Alec Radford, Jeff Wu, R. Child, David Luan, Dario Amodei and Ilya Sutskever · 2019
Cited alongside, same era.
“SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”
“Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start”
Wenpeng Yin, Nazneen Rajani, Dragomir Radev, Richard Socher and Caiming Xiong · 2020
Later among the works it cites.
“Making Pre-trained Language Models Better Few-shot Learners”
Tianyu Gao, Adam Fisch and Danqi Chen · 2021
Closest in time.
“The Power of Scale for Parameter-Efficient Prompt Tuning”
Brian Lester, Rami Al-Rfou and Noah Constant · 2021
Closest in time.
“Prefix-Tuning: Optimizing Continuous Prompts for Generation”
Xiang Li and Percy Liang · 2021
Closest in time.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang and Jie Tang · 2021
Closest in time.
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Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy and Samuel Bowman · 2019
Cited alongside, same era.
“Language Models are Few-Shot Learners”
Tom 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris 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
Cited alongside, same era.
“The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding”
Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon and Guihong Cao · 2020
Cited alongside, same era.
“Uncertainty-aware Self-training for Few-shot Text Classification”
Subhabrata Mukherjee and Ahmed Awadallah · 2020
Cited alongside, same era.
“Adversarial NLI: A New Benchmark for Natural Language Understanding”
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston and Douwe Kiela · 2020
Cited alongside, same era.
“Zero-Shot Cross-Lingual Transfer with Meta Learning”
Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva and Isabelle Augenstein · 2020
Cited alongside, same era.
“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. Liu · 2020
Cited alongside, same era.
“Cutting down on prompts and parameters: Simple few-shot learning with language models”
Robert Logan, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh and Sebastian Riedel · 2021
Closest in time.
“True Few-Shot Learning with Language Models”
Ethan Perez, Douwe Kiela and Kyunghyun Cho · 2021
Closest in time.
“Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference”
Timo Schick and Hinrich Schütze · 2021
Closest in time.
“It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners”
Timo Schick and Hinrich Schütze · 2021
Closest in time.
“LiST: Lite Self-training Makes Efficient Few-shot Learners”
Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Awadallah and Jianfeng Gao · 2021
Closest in time.
“FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark”
Liang Xu, Xiaojing Lu, Chenyang Yuan, Xuanwei Zhang, Hu Yuan, Huilin Xu, Guoao Wei, Xiang Pan and Hai Hu · 2021
Closest in time.
“CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP”
Qinyuan Ye, Bill Lin and Xiang Ren · 2021
Closest in time.
“Revisiting Few-sample {BERT} Fine-tuning”
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Weinberger and Yoav Artzi · 2021
Closest in time.
“Meta Label Correction for Noisy Label Learning”
Guoqing Zheng, Ahmed Awadallah and Susan Dumais · 2021
Closest in time.
“FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding”
Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Jian Li, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder and Zhilin Yang · 2021
Closest in time.