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Prompt-based methods with large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks.
Multiwoz 2.1: Multi-domain dialogue state corrections and state tracking baselines
Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyag Gao, and Dilek Hakkani-Tur. 2019 · 1907
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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, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff 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 · 2005
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A simple language model for task-oriented dialogue
Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, and Richard Socher. 2020 · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006 · 2006
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Xiaoxue Zang, Abhinav Rastogi, Srinivas Sunkara, Raghav Gupta, Jianguo Zhang, and Jindong Chen. 2020 · 2007
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The second dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and Jason D Williams. 2014 · 2014
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Key-value retrieval networks for task-oriented dialogue
Mihail Eric, Lakshmi Krishnan, Francois Charette, and Christopher D. Manning. 2017 · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, P. Abbeel, and Sergey Levine. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
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Multiwoz - A large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gasic. 2018 · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman. 2018 · 2018
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Building a conversational agent overnight with dialogue self-play
Pararth Shah, Dilek Hakkani-Tür, Gokhan Tür, Abhinav Rastogi, Ankur Bapna, Neha Nayak, and Larry Heck. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam M. Shazeer and Mitchell Stern. 2018 · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Earlier work this paper cites.
Transferable multi-domain state generator for task-oriented dialogue systems
Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, Caiming Xiong, Richard Socher, and Pascale Fung. 2019 · 2019
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Zero-shot transfer learning with synthesized data for multi-domain dialogue state tracking
Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, and Monica Lam. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020 · 2020
Cited alongside, same era.
TripPy: A triple copy strategy for value independent neural dialog state tracking
Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi, and Milica Gasic. 2020 · 2020
Cited alongside, same era.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Cited alongside, same era.
Efficient dialogue state tracking by selectively overwriting memory
Sungdong Kim, Sohee Yang, Gyuwan Kim, and Sang-Woo Lee. 2020 · 2020
Cited alongside, same era.
Modeling long context for task-oriented dialogue state generation
Jun Quan and Deyi Xiong. 2020 · 2020
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021b · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Few-shot bot: Prompt-based learning for dialogue systems
Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, and Pascale Fung. 2021 · 2021
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Self-training improves pre-training for few-shot learning in task-oriented dialog systems
Fei Mi, Wanhao Zhou, Lingjing Kong, Fengyu Cai, Minlie Huang, and Boi Faltings. 2021 · 2021
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2020 · 2020
Cited alongside, same era.
How to tame your data: Data augmentation for dialog state tracking
Adam Summerville, Jordan Hashemi, James Ryan, and William Ferguson. 2020 · 2020
Cited alongside, same era.
Dialog state tracking with reinforced data augmentation
Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu. 2020 · 2020
Cited alongside, same era.
Reason first, then respond: Modular generation for knowledge-infused dialogue
Leonard Adolphs, Kurt Shuster, Jack Urbanek, Arthur Szlam, and Jason Weston. 2021 · 2021
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Action-based conversations dataset: A corpus for building more in-depth task-oriented dialogue systems
Derek Chen, Howard Chen, Yi Yang, Alexander Lin, and Zhou Yu. 2021 · 2021
Cited alongside, same era.
Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2021 · 2021
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Soloist: Building task bots at scale with transfer learning and machine teaching
Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, and Jianfeng Gao. 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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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2021 · 2021
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Meta-learning via language model in-context tuning
Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, and He He. 2022 · 2022
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In-context learning for few-shot dialogue state tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A. Smith, and Mari Ostendorf. 2022 · 2022
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Are prompt-based models clueless?
Pride Kavumba, Ryo Takahashi, and Yusuke Oda. 2022 · 2022
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Internet-augmented dialogue generation
Mojtaba Komeili, Kurt Shuster, and Jason Weston. 2022 · 2022
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What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
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Dialogue summaries as dialogue states (DS2), template-guided summarization for few-shot dialogue state tracking
Jamin Shin, Hangyeol Yu, Hyeongdon Moon, Andrea Madotto, and Juneyoung Park. 2022 · 2022
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Retrieval-free knowledge-grounded dialogue response generation with adapters
Yan Xu, Etsuko Ishii, Samuel Cahyawijaya, Zihan Liu, Genta Indra Winata, Andrea Madotto, Dan Su, and Pascale Fung. 2022 · 2022
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Prompt learning for few-shot dialogue state tracking
Yuting Yang, Wenqiang Lei, Juan Cao, Jintao Li, and Tat-Seng Chua. 2022 · 2022
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