Fetching the paper…
Reading the bibliography…
Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks.
Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, A. Lazaridou, Wang Ling, L. Yu, Chris Dyer, and P. Blunsom. 2019 · 1901
Earlier work this paper cites.
UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
Earlier work this paper cites.
CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 1967
Earlier work this paper cites.
Toward semantics-based answer pinpointing
Eduard Hovy, Laurie Gerber, Ulf Hermjakob, Chin-Yew Lin, and Deepak Ravichandran. 2001 · 2001
Earlier work this paper cites.
Exploiting cloze questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020a · 2001
Earlier work this paper cites.
Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
Earlier work this paper cites.
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, et al. 2020 · 2005
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
The second pascal recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006 · 2006
Earlier work this paper cites.
Ethos: an online hate speech detection dataset
Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos, and Grigorios Tsoumakas. 2020 · 2006
Earlier work this paper cites.
The third pascal recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
Earlier work this paper cites.
The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009 · 2009
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. 2020b · 2009
Earlier work this paper cites.
Contributions to the study of sms spam filtering: New collection and results
Tiago A. Almeida, José María G. Hidalgo, and Akebo Yamakami. 2011 · 2011
Earlier work this paper cites.
Robust disambiguation of named entities in text
Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum. 2011 · 2011
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
Climate-fever: A dataset for verification of real-world climate claims
T. Diggelmann, Jordan L. Boyd-Graber, Jannis Bulian, Massimiliano Ciaramita, and Markus Leippold. 2020 · 2012
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, A. Fisch, and Danqi Chen. 2020 · 2012
Earlier work this paper cites.
SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew Gordon, Zornitsa Kozareva, and Melissa Roemmele. 2012 · 2012
Earlier work this paper cites.
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
Harsha Gurulingappa, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo. 2012 · 2012
Earlier work this paper cites.
The winograd schema challenge
Hector J. Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
Earlier work this paper cites.
Hatexplain: A benchmark dataset for explainable hate speech detection
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2020 · 2012
Earlier work this paper cites.
Annotated Gigaword
Courtney Napoles, Matthew Gormley, and Benjamin Van Durme. 2012 · 2012
Earlier work this paper cites.
English-asl gloss parallel corpus 2012: Aslg-pc12
A. Othman and M. Jemni. 2012 · 2012
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.
Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and J. Leskovec. 2013 · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Good debt or bad debt: Detecting semantic orientations in economic texts
Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Pyry Takala. 2014 · 2014
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.
Question-answer driven semantic role labeling: Using natural language to annotate natural language
Luheng He, Mike Lewis, and Luke Zettlemoyer. 2015 · 2015
Earlier work this paper cites.
Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, D. Kontokostas, Pablo N. Mendes, Sebastian Hellmann, M. Morsey, Patrick van Kleef, S. Auer, and C. Bizer. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
Earlier work this paper cites.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 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.
Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
Earlier work this paper cites.
Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V. U. Güney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
Earlier work this paper cites.
“liar, liar pants on fire”: A new benchmark dataset for fake news detection
William Yang Wang. 2017 · 2017
Earlier work this paper cites.
Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017 · 2017
Earlier work this paper cites.
Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
Learning to split and rephrase from Wikipedia edit history
Jan A. Botha, Manaal Faruqui, John Alex, Jason Baldridge, and Dipanjan Das. 2018 · 2018
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
T-REx: A large scale alignment of natural language with knowledge base triples
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl. 2018 · 2018
Earlier work this paper cites.
Identifying well-formed natural language questions
Manaal Faruqui and Dipanjan Das. 2018 · 2018
Earlier work this paper cites.
Hate Speech Dataset from a White Supremacy Forum
Ona de Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. 2018 · 2018
Earlier work this paper cites.
Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, and Kyunghyun Cho. 2018 · 2018
Earlier work this paper cites.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Earlier work this paper cites.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 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.
The natural language decathlon: Multitask learning as question answering
Bryan McCann, N. Keskar, Caiming Xiong, and R. Socher. 2018 · 2018
Cited alongside, same era.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Cited alongside, same era.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
“going on a vacation” takes longer than “going for a walk”: A study of temporal commonsense understanding
Ben Zhou, Daniel Khashabi, Qiang Ning, and Dan Roth. 2019 · 2019
Later among the works it cites.
Self-supervised meta-learning for few-shot natural language classification tasks
Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, and Andrew McCallum. 2020b · 2020
Later among the works it cites.
TweetEval: Unified benchmark and comparative evaluation for tweet classification
Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, and Leonardo Neves. 2020 · 2020
Later among the works it cites.
Beat the AI: Investigating adversarial human annotation for reading comprehension
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020 · 2020
Later among the works it cites.
Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen tau Yih, and Yejin Choi. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
Cited alongside, same era.
DuoRC: Towards complex language understanding with paraphrased reading comprehension
Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, and Karthik Sankaranarayanan. 2018 · 2018
Cited alongside, same era.
CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
Cited alongside, same era.
FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
Cited alongside, same era.
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification
Sowmya Vajjala and Ivana Lučić. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
WikiQA: A challenge dataset for open-domain question answering
Yi Yang, Wen-tau Yih, and Christopher Meek. 2015 · 2018
Cited alongside, same era.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
Later among the works it cites.
ProtoQA: A question answering dataset for prototypical common-sense reasoning
Michael Boratko, Xiang Li, Tim O’Gorman, Rajarshi Das, Dan Le, and Andrew McCallum. 2020 · 2020
Later among the works it cites.
MOCHA: A dataset for training and evaluating generative reading comprehension metrics
Anthony Chen, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2020a · 2020
Later among the works it cites.
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2020 · 2020
Later among the works it cites.
Neural CRF model for sentence alignment in text simplification
Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, and Wei Xu. 2020 · 2020
Later among the works it cites.
Qasc: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
Later among the works it cites.
Explainable automated fact-checking for public health claims
Neema Kotonya and Francesca Toni. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020a · 2020
Later among the works it cites.
CommonGen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020b · 2020
Later among the works it cites.
How can we accelerate progress towards human-like linguistic generalization?
Tal Linzen. 2020 · 2020
Later among the works it cites.
“I’d rather just go to bed”: Understanding indirect answers
Annie Louis, Dan Roth, and Filip Radlinski. 2020 · 2020
Later among the works it cites.
LiMiT: The literal motion in text dataset
Irene Manotas, Ngoc Phuoc An Vo, and Vadim Sheinin. 2020 · 2020
Later among the works it cites.
Effective transfer learning for identifying similar questions: Matching user questions to covid-19 faqs
Clara H. McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, and Xavier Amatriain. 2020 · 2020
Later among the works it cites.
Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
Later among the works it cites.
Zero-shot cross-lingual transfer with meta learning
Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, and Isabelle Augenstein. 2020 · 2020
Later among the works it cites.
BioMRC: A dataset for biomedical machine reading comprehension
Dimitris Pappas, Petros Stavropoulos, Ion Androutsopoulos, and Ryan McDonald. 2020 · 2020
Later among the works it cites.
How context affects language models’ factual predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2020 · 2020
Later among the works it cites.
What does this acronym mean? introducing a new dataset for acronym identification and disambiguation
Amir Pouran Ben Veyseh, Franck Dernoncourt, Quan Hung Tran, and Thien Huu Nguyen. 2020 · 2020
Later among the works it cites.
Intermediate-task transfer learning with pretrained language models: When and why does it work?
Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020 · 2020
Later among the works it cites.
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 J. Liu. 2020 · 2020
Later among the works it cites.
Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
Later among the works it cites.
Getting closer to ai complete question answering: A set of prerequisite real tasks
Anna Rogers, Olga Kovaleva, Matthew Downey, and Anna Rumshisky. 2020 · 2020
Later among the works it cites.
Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Investigating societal biases in a poetry composition system
Emily Sheng and David Uthus. 2020 · 2020
Later among the works it cites.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Later among the works it cites.
Which tasks should be learned together in multi-task learning?
Trevor Scott Standley, A. Zamir, Dawn Chen, L. Guibas, Jitendra Malik, and S. Savarese. 2020 · 2020
Later among the works it cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle. 2020 · 2020
Later among the works it cites.
Exploring and predicting transferability across NLP tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer. 2020 · 2020
Later among the works it cites.
To pretrain or not to pretrain: Examining the benefits of pretrainng on resource rich tasks
Sinong Wang, Madian Khabsa, and Hao Ma. 2020 · 2020
Later among the works it cites.
Blimp: The benchmark of linguistic minimal pairs for english
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020 · 2020
Later among the works it cites.
Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang R. Zhang, and Christopher Ré. 2020 · 2020
Later among the works it cites.
Universal natural language processing with limited annotations: Try few-shot textual entailment as a start
Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2020
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. 2020 · 2020
Later among the works it cites.
Semi-supervised URL segmentation with recurrent neural networks pre-trained on knowledge graph entities
Hao Zhang, Jae Ro, and Richard Sproat. 2020 · 2020
Later among the works it cites.
Flex: Unifying evaluation for few-shot nlp
Jonathan Bragg, Arman Cohan, Kyle Lo, and Iz Beltagy. 2021 · 2021
Closest in time.
Lifelong learning of few-shot learners across nlp tasks
Xisen Jin, Mohammad Rostami, and Xiang Ren. 2021 · 2021
Closest in time.
Text modular networks: Learning to decompose tasks in the language of existing models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2021 · 2021
Closest in time.
Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, A. Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario vSavsko, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clement Delangue, Th’eo Matussiere, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, Franccois Lagunas, Alexander M. Rush, and Thomas Wolf. 2021 · 2021
Closest in time.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2021 · 2021
Closest in time.
Dreca: A general task augmentation strategy for few-shot natural language inference
Shikhar Murty, T. Hashimoto, and Christopher D. Manning. 2021 · 2021
Closest in time.
Improving and simplifying pattern exploiting training
Derek Tam, R. R. Menon, M. Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
Closest in time.
Entailment as few-shot learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma. 2021 · 2021
Closest in time.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2021 · 2021
Closest in time.
Revisiting few-sample {bert} fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2021 · 2021
Closest in time.
Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and D. Klein. 2021 · 2021
Closest in time.