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We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 · 1901
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Signature verification using a ”siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1993 · 1993
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Evidence for universality and cultural variation of differential emotion response patterning
K. Scherer and H. G. Wallbott. 1994 · 1994
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Support vector machines
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. 1998 · 1998
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Statistical significance tests for machine translation evaluation
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
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Emotions from text: machine learning for text-based emotion prediction
Cecilia Ovesdotter Alm, Dan Roth, and Richard Sproat. 2005 · 2005
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Perceptions of emotions in expressive storytelling
Cecilia Ovesdotter Alm and Richard Sproat. 2005 · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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Computing semantic relatedness using wikipedia-based explicit semantic analysis
Evgeniy Gabrilovich and Shaul Markovitch. 2007 · 2007
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Affect in* text and speech
Ebba Cecilia Ovesdotter Alm. 2008 · 2008
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Importance of semantic representation: Dataless classification
Ming-Wei Chang, Lev-Arie Ratinov, D. Roth, and Vivek Srikumar. 2008 · 2008
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Zero-data learning of new tasks
Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio. 2008 · 2008
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Natural Language Processing with Python , 1st edition
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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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
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
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#Emotional Tweets
Saif Mohammad. 2012 · 2012
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Efficient estimation of word representations in vector space
Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and J. Dean. 2014 · 2014
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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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Detecting emotion stimuli in emotion-bearing sentences
Diman Ghazi, Diana Inkpen, and Stan Szpakowicz. 2015 · 2015
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Sentiment, emotion, purpose, and style in electoral tweets
Saif M Mohammad, Xiaodan Zhu, Svetlana Kiritchenko, and Joel Martin. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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The emotion in text, published by crowdflower
Crowdflower. 2016 · 2016
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SemEval-2016 task 4: Sentiment analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, and Fabrizio Sebastiani. 2016 · 2016
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A Twitter corpus and benchmark resources for German sentiment analysis
Mark Cieliebak, Jan Milan Deriu, Dominic Egger, and Fatih Uzdilli. 2017 · 2017
Crowdsourcing and validating event-focused emotion corpora for German and English
Enrica Troiano, Sebastian Padó, and Roman Klinger. 2019 · 2019
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HEAD-QA: A healthcare dataset for complex reasoning
David Vilares and Carlos Gómez-Rodríguez. 2019 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019 · 2019
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Task-aware representation of sentences for generic text classification
Kishaloy Halder, Alan Akbik, Josip Krapac, and Roland Vollgraf. 2020 · 2020
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The multilingual Amazon reviews corpus
Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. 2020 · 2020
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Efficient natural language response suggestion for smart reply
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Dailydialog: A manually labelled multi-turn dialogue dataset
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017 · 2017
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Grounded emotions
V. Liu, C. Banea, and R. Mihalcea. 2007 · 2017
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Stance and sentiment in tweets
Saif M Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017 · 2017
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Spanish corpus for sentiment analysis towards brands
María Navas-Loro, Víctor Rodríguez-Doncel, Idafen Santana-Perez, and Alberto Sánchez. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 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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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
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben-Zaken, Shauli Ravfogel, and Yoav Goldberg. 2021 · 2021
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Ten thousand german news articles dataset
Timo Block. 2019 · 2021
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Unsupervised label refinement improves dataless text classification
Zewei Chu, Karl Stratos, and Kevin Gimpel. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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AG’s corpus of news articles
Antonio Gulli. 2005 · 2021
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WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021 · 2021
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How many data points is a prompt worth?
Teven Le Scao and Alexander Rush. 2021 · 2021
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Cutting down on prompts and parameters: Simple few-shot learning with language models
Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
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Entailment as few-shot learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma. 2021 · 2021
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