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This work focuses on in-context data augmentation for intent detection.
Roberta: A robustly optimized BERT pretraining approach
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CTRL: A conditional transformer language model for controllable generation
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Few-shot intent detection via contrastive pre-training and fine-tuning
Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, and Philip Yu. 2021b · 1912
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The ATIS spoken language systems pilot corpus
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Less is more: Active learning with support vector machines
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Toward optimal active learning through sampling estimation of error reduction
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel. 2001 · 2001
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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2003
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DIET: lightweight language understanding for dialogue systems
Tanja Bunk, Daksh Varshneya, Vladimir Vlasov, and Alan Nichol. 2020 · 2004
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Active learning to recognize multiple types of plankton
Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, and Thomas Hopkins. 2004 · 2004
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum. 2005 · 2005
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Let’s go public! taking a spoken dialog system to the real world
Antoine Raux, Brian Langner, Dan Bohus, Alan W. Black, and Maxine Eskénazi. 2005 · 2005
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Dialoglue: A natural language understanding benchmark for task-oriented dialogue
Shikib Mehri, Mihail Eric, and Dilek Hakkani-Tür. 2020 · 2009
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Pomdp-based statistical spoken dialog systems: A review
Steve J. Young, Milica Gasic, Blaise Thomson, and Jason D. Williams. 2013 · 2013
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016 · 2016
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Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, and Joseph Dureau. 2018 · 2018
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Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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A deep generative framework for paraphrase generation
Ankush Gupta, Arvind Agarwal, Prawaan Singh, and Piyush Rai. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Y. Zou. 2019 · 2019
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A closer look at feature space data augmentation for few-shot intent classification
Varun Kumar, Hadrien Glaude, Cyprien de Lichy, and Wlliam Campbell. 2019 · 2019
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An evaluation dataset for intent classification and out-of-scope prediction
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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Benchmarking natural language understanding services for building conversational agents
Xingkun Liu, Arash Eshghi, Pawel Swietojanski, and Verena Rieser. 2019a · 2019
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Coco: Controllable counterfactuals for evaluating dialogue state trackers
Shiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu, Nazneen Fatema Rajani, Xifeng Yan, Yingbo Zhou, and Caiming Xiong. 2021 · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Example-driven intent prediction with observers
Shikib Mehri and Mihail Eric. 2021 · 2021
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Language model is all you need: Natural language understanding as question answering
Mahdi Namazifar, Alexandros Papangelis, Gökhan Tür, and Dilek Hakkani-Tür. 2021 · 2021
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Multilingual paraphrase generation for bootstrapping new features in task-oriented dialog systems
Subhadarshi Panda, Caglar Tirkaz, Tobias Falke, and Patrick Lehnen. 2021 · 2021
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Do not have enough data? deep learning to the rescue!
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Language models are few-shot learners
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Data manipulation: Towards effective instance learning for neural dialogue generation via learning to augment and reweight
Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, and Dawei Yin. 2020 · 2020
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Efficient intent detection with dual sentence encoders
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ConveRT: Efficient and accurate conversational representations from transformers
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Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar, Seokhwan Kim, Gokhan Tur, and Dilek Hakkani-Tur. 2021 · 2021
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Data augmentation for spoken language understanding via pretrained language models
Baolin Peng, Chenguang Zhu, Michael Zeng, and Jianfeng Gao. 2021 · 2021
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GPT3Mix: Leveraging large-scale language models for text augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyoung Park. 2021 · 2021
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Few-shot intent classification and slot filling with retrieved examples
Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, and Qi Li. 2021 · 2021
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Effectiveness of pre-training for few-shot intent classification
Haode Zhang, Yuwei Zhang, Li-Ming Zhan, Jiaxin Chen, Guangyuan Shi, Xiao-Ming Wu, and Albert Y.S. Lam. 2021a · 2021
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Understanding dataset difficulty with V -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta. 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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Typical decoding for natural language generation
Clara Meister, Tiago Pimentel, Gian Wiher, and Ryan Cotterell. 2022 · 2022
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Prioritized training on points that are learnable, worth learning, and not yet learnt
Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, and Yarin Gal. 2022 · 2022
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Data augmentation with paraphrase generation and entity extraction for multimodal dialogue system
Eda Okur, Saurav Sahay, and Lama Nachman. 2022 · 2022
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Data augmentation for intent classification with off-the-shelf large language models
Gaurav Sahu, Pau Rodriguez, Issam Laradji, Parmida Atighehchian, David Vazquez, and Dzmitry Bahdanau. 2022 · 2022
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Few-shot semantic parsing with language models trained on code
Richard Shin and Benjamin Van Durme. 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, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
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