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This paper investigates the effectiveness of pre-training for few-shot intent classification.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019 · 2019
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
A simple language model for task-oriented dialogue
Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, and Richard Socher. 2020 · 2020
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Dynamic semantic matching and aggregation network for few-shot intent detection
Hoang Nguyen, Chenwei Zhang, Congying Xia, and Philip Yu. 2020 · 2020
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TOD-BERT: Pre-trained natural language understanding for task-oriented dialogue
Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, and Caiming Xiong. 2020 · 2020
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Composed variational natural language generation for few-shot intents
Congying Xia, Caiming Xiong, Philip Yu, and Richard Socher. 2020a · 2020
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Composed variational natural language generation for few-shot intents
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Later among the works it cites.
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Cross-lingual transfer learning for intent detection of covid-19 utterances
Abhinav Arora, Akshat Shrivastava, Mrinal Mohit, Lorena Sainz-Maza Lecanda, and Ahmed Aly. 2020a
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HINT3: Raising the bar for intent detection in the wild
Gaurav Arora, Chirag Jain, Manas Chaturvedi, and Krupal Modi. 2020b
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Intent detection with WikiHow
Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020b
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Jianguo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip Yu, Richard Socher, and Caiming Xiong. 2020a · 2020
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