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Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.
Language models are few-shot learners
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Do speakers and listeners observe the gricean maxim of quantity?
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Dialogue distillation: Open-domain dialogue augmentation using unpaired data
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The chime corpus: a resource and a challenge for computational hearing in multisource environments
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Mpc: A multi-party chat corpus for modeling social phenomena in discourse
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Quality control in crowdsourcing systems: Issues and directions
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Evaluation on crowdsourcing research: Current status and future direction
Yuxiang Zhao and Qinghua Zhu. 2014 · 2014
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The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems
Ryan Lowe, Nissan Pow, Iulian Vlad Serban, and Joelle Pineau. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B Dolan. 2016 · 2016
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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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Personalizing dialogue agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018 · 2018
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Build it break it fix it for dialogue safety: Robustness from adversarial human attack
Emily Dinan, Samuel Humeau, Bharath Chintagunta, and Jason Weston. 2019 · 2019
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Topical-chat: Towards knowledge-grounded open-domain conversations
Karthik Gopalakrishnan, Behnam Hedayatnia, Qinglang Chen, Anna Gottardi, Sanjeev Kwatra, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tür, and Amazon Alexa AI. 2019 · 2019
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Insufficient data can also rock! learning to converse using smaller data with augmentation
Juntao Li, Lisong Qiu, Bo Tang, Dongmin Chen, Dongyan Zhao, and Rui Yan. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Meld: A multimodal multi-party dataset for emotion recognition in conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2019 · 2019
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Domain adaptive dialog generation via meta learning
Kun Qian and Zhou Yu. 2019 · 2019
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Towards empathetic open-domain conversation models: A new benchmark and dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019 · 2019
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Dialogue natural language inference
Sean Welleck, Jason Weston, Arthur Szlam, and Kyunghyun Cho. 2019 · 2019
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Low-resource knowledge-grounded dialogue generation
Xueliang Zhao, Wei Wu, Chongyang Tao, Can Xu, Dongyan Zhao, and Rui Yan. 2019 · 2019
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The pushshift reddit dataset
Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020 · 2020
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Mpdd: A multi-party dialogue dataset for analysis of emotions and interpersonal relationships
Yi-Ting Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020 · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
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Textgail: Generative adversarial imitation learning for text generation
Qingyang Wu, Lei Li, and Zhou Yu. 2021 · 2021
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Using large language models to simulate multiple humans
Gati Aher, Rosa I Arriaga, and Adam Tauman Kalai. 2022 · 2022
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Building a role specified open-domain dialogue system leveraging large-scale language models
Sanghwan Bae, Donghyun Kwak, Sungdong Kim, Donghoon Ham, Soyoung Kang, Sang-Woo Lee, and Woomyoung Park. 2022 · 2022
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Weakly supervised data augmentation through prompting for dialogue understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Andy Rosenbaum, Seokhwan Kim, Yang Liu, Zhou Yu, and Dilek Hakkani-Tur. 2022 · 2022
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Learning to improve persona consistency in multi-party dialogue generation via text knowledge enhancement
Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, and Yifei Zhang. 2022 · 2022
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Aaron W Li, Veronica Jiang, Steven Y Feng, Julia Sprague, Wei Zhou, and Jesse Hoey. 2020 · 2020
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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, Peter J Liu, et al. 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, Rémi Louf, Morgan Funtowicz, et al. 2020 · 2020
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How robust are fact checking systems on colloquial claims?
Byeongchang Kim, Hyunwoo Kim, Seokhee Hong, and Gunhee Kim. 2021a · 2021
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Augpt: Auxiliary tasks and data augmentation for end-to-end dialogue with pre-trained language models
Jonáš Kulhánek, Vojtěch Hudeček, Tomáš Nekvinda, and Ondřej Dušek. 2021 · 2021
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Legoeval: An open-source toolkit for dialogue system evaluation via crowdsourcing
Yu Li, Josh Arnold, Feifan Yan, Weiyan Shi, and Zhou Yu. 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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Knowledge-consistent dialogue generation with knowledge graphs
Minki Kang, Jin Myung Kwak, Jinheon Baek, and Sung Ju Hwang. 2022 · 2022
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Soda: Million-scale dialogue distillation with social commonsense contextualization
Hyunwoo Kim, Jack Hessel, Liwei Jiang, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, et al. 2022 · 2022
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Lad: Language models as data for zero-shot dialog
Shikib Mehri, Yasemin Altun, and Maxine Eskenazi. 2022 · 2022
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Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Godel: Large-scale pre-training for goal-directed dialog
Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, and Jianfeng Gao. 2022 · 2022
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Data augmentation for intent classification with off-the-shelf large language models
Gaurav Sahu, Pau Rodriguez, Issam H Laradji, Parmida Atighehchian, David Vazquez, and Dzmitry Bahdanau. 2022 · 2022
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Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage
Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, et al. 2022 · 2022
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Promda: Prompt-based data augmentation for low-resource nlu tasks
Yufei Wang, Can Xu, Qingfeng Sun, Huang Hu, Chongyang Tao, Xiubo Geng, and Daxin Jiang. 2022 · 2022
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Learning new skills after deployment: Improving open-domain internet-driven dialogue with human feedback
Jing Xu, Megan Ung, Mojtaba Komeili, Kushal Arora, Y-Lan Boureau, and Jason Weston. 2022 · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
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Multi-party empathetic dialogue generation: A new task for dialog systems
Ling.Yu Zhu, Zhengkun Zhang, Jun Wang, Hongbin Wang, Haiying Wu, and Zhenglu Yang. 2022 · 2022
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Can you put it all together: Evaluating conversational agents’ ability to blend skills
Eric Michael Smith, Mary Williamson, Kurt Shuster, Jason Weston, and Y-Lan Boureau. 2020 · 2030
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