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Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
Multi-domain conversation quality evaluation via user satisfaction estimation
Praveen Kumar Bodigutla, Lazaros Polymenakos, and Spyros Matsoukas. 2019 · 1911
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PARADISE: A framework for evaluating spoken dialogue agents
Marilyn A. Walker, Diane J. Litman, Candace A. Kamm, and Alicia Abella. 1997 · 1997
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Joint turn and dialogue level user satisfaction estimation on multi-domain conversations
Praveen Kumar Bodigutla, Aditya Tiwari, Josep Valls-Vargas, Lazaros Polymenakos, and Spyros Matsoukas. 2020 · 2010
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Interaction quality: Assessing the quality of ongoing spoken dialog interaction by experts - and how it relates to user satisfaction
Alexander Schmitt and Stefan Ultes. 2015 · 2015
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Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, and Qi Zhang. 2019 · 2019
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Predicting user intents and satisfaction with dialogue-based conversational recommendations
Wanling Cai and Li Chen. 2020 · 2020
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Multiwoz 2.1: A consolidated multi-domain dialogue dataset with state corrections and state tracking baselines
Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Kumar Goyal, Peter Ku, and Dilek Hakkani-Tür. 2020 · 2020
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Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2020 · 2020
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Survey on evaluation methods for dialogue systems
Jan Deriu, Álvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, and Mark Cieliebak. 2021 · 2021
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Self-supervised contrastive learning for efficient user satisfaction prediction in conversational agents
Mohammad Kachuee, Hao Yuan, Young-Bum Kim, and Sungjin Lee. 2021a · 2021
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Self-supervised contrastive learning for efficient user satisfaction prediction in conversational agents
Mohammad Kachuee, Hao Yuan, Young-Bum Kim, and Sungjin Lee. 2021b · 2021
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Turn-level user satisfaction estimation in E-commerce customer service
Runze Liang, Ryuichi Takanobu, Feng-Lin Li, Ji Zhang, Haiqing Chen, and Minlie Huang. 2021 · 2021
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Simulating user satisfaction for the evaluation of task-oriented dialogue systems
Weiwei Sun, Shuo Zhang, Krisztian Balog, Zhaochun Ren, Pengjie Ren, Zhumin Chen, and Maarten de Rijke. 2021 · 2021
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Constitutional AI: harmlessness from AI feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosiute, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemí Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022 · 2022
Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A. Hale. 2023 · 2023
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Understanding user satisfaction with task-oriented dialogue systems
Clemencia Siro, Mohammad Aliannejadi, and Maarten de Rijke. 2022 · 2023
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A speaker turn-aware multi-task adversarial network for joint user satisfaction estimation and sentiment analysis
Kaisong Song, Yangyang Kang, Jiawei Liu, Xurui Li, Changlong Sun, and Xiaozhong Liu. 2023 · 2023
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Text classification via large language models
Xiaofei Sun, Xiaoya Li, Jiwei Li, Fei Wu, Shangwei Guo, Tianwei Zhang, and Guoyin Wang. 2023 · 2023
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Explanation selection using unlabeled data for chain-of-thought prompting
Xi Ye and Greg Durrett. 2023 · 2023
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Cited alongside, same era.
User satisfaction estimation with sequential dialogue act modeling in goal-oriented conversational systems
Yang Deng, Wenxuan Zhang, Wai Lam, Hong Cheng, and Helen Meng. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory W. Mathewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane Wang, and Felix Hill. 2022 · 2022
Cited alongside, same era.
User satisfaction modeling with domain adaptation in task-oriented dialogue systems
Yan Pan, Mingyang Ma, Bernhard Pflugfelder, and Georg Groh. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Unlocking the potential of user feedback: Leveraging large language model as user simulators to enhance dialogue system
Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, and Aldo Lipani. 2023 · 2023
Cited alongside, same era.
Can large language models explain themselves? A study of llm-generated self-explanations
Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, and Leilani H. Gilpin. 2023 · 2023
Cited alongside, same era.
Modeling user satisfaction dynamics in dialogue via hawkes process
Yilmaz Emine Ye Fanghua, Hu Zhiyuan. 2023 · 2023
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