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Intent detection is a critical component of task-oriented dialogue systems (TODS) which enables the identification of suitable actions to address user utterances at each dialog turn.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2004
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Attention-based recurrent neural network models for joint intent detection and slot filling
Bing Liu and Ian R. Lane. 2016 · 2016
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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An evaluation dataset for intent classification and out-of-scope prediction
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HINT3: Raising the bar for intent detection in the wild
Gaurav Arora, Chirag Jain, Manas Chaturvedi, and Krupal Modi. 2020 · 2020
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Efficient intent detection with dual sentence encoders
Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, and Ivan Vulić. 2020 · 2020
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Keybert: Minimal keyword extraction with bert
Maarten Grootendorst. 2020 · 2020
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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. 2021 · 2021
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Efficient few-shot learning without prompts
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Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022b · 2024
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Beyond the known: Investigating llms performance on out-of-domain intent detection
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