Fetching the paper…
Reading the bibliography…
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query.
Benchmarking natural language understanding services for building conversational agents
Xingkun Liu, Arash Eshghi, Pawel Swietojanski, and Verena Rieser. 2019 · 1903
Earlier work this paper cites.
Deep unknown intent detection with margin loss
Ting-En Lin and Hua Xu. 2019 · 1906
Earlier work this paper cites.
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, et al. 2019 · 1909
Earlier work this paper cites.
The atis spoken language systems pilot corpus
Charles T Hemphill, John J Godfrey, and George R Doddington. 1990 · 1990
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
Earlier work this paper cites.
Data domain description using support vectors
David M. J. Tax and Robert P. W. Duin. 1999 · 1999
Earlier work this paper cites.
Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang. 2020 · 2006
Earlier work this paper cites.
Hint3: Raising the bar for intent detection in the wild
Gaurav Arora, Chirag Jain, Manas Chaturvedi, and Krupal Modi. 2020 · 2009
Earlier work this paper cites.
Benchmarking intent detection for task-oriented dialog systems
Haode Qi, Lin Pan, Atin Sood, Abhishek Shah, Ladislav Kunc, and Saloni Potdar. 2020 · 2012
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
Cited alongside, same era.
Principled detection of out-of-distribution examples in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Enhancing the generalization for intent classification and out-of-domain detection in SLU
Yilin Shen, Yen-Chang Hsu, Avik Ray, and Hongxia Jin. 2021 · 2021
Later among the works it cites.
Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Zijun Liu, Yanan Wu, Hong Xu, Huixing Jiang, and Weiran Xu. 2021 · 2021
Later among the works it cites.
Out-of-scope intent detection with self-supervision and discriminative training
Li-Ming Zhan, Haowen Liang, Bo Liu, Lu Fan, Xiao-Ming Wu, and Albert Y. S. Lam. 2021 · 2021
Later among the works it cites.
Benchmarking language-agnostic intent classification for virtual assistant platforms
Gengyu Wang, Cheng Qian, Lin Pan, Haode Qi, Ladislav Kunc, and Saloni Potdar. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Out-of-domain detection based on generative adversarial network
Seonghan Ryu, Sangjun Koo, Hwanjo Yu, and Gary Geunbae Lee. 2018 · 2018
Cited alongside, same era.
Efficient intent detection with dual sentence encoders
Iñigo Casanueva, Tadas Temcinas, Daniela Gerz, Matthew Henderson, and Ivan Vulic. 2020 · 2020
Cited alongside, same era.
Likelihood ratios and generative classifiers for unsupervised out-of-domain detection in task oriented dialog
Varun Gangal, Abhinav Arora, Arash Einolghozati, and Sonal Gupta. 2020 · 2020
Cited alongside, same era.
Outflip: Generating out-of-domain samples for unknown intent detection with natural language attack
DongHyun Choi, Myeongcheol Shin, EungGyun Kim, and Dong Ryeol Shin. 2021 · 2021
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2018a
Cited in the paper.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018b
Cited in the paper.
Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, and Weiran Xu. 2022 · 2022
Later among the works it cites.
Are pre-trained transformers robust in intent classification? a missing ingredient in evaluation of out-of-scope intent detection
Jianguo Zhang, Kazuma Hashimoto, Yao Wan, Zhiwei Liu, Ye Liu, Caiming Xiong, and Philip Yu. 2022 · 2022
Later among the works it cites.
KNN-contrastive learning for out-of-domain intent classification
Yunhua Zhou, Peiju Liu, and Xipeng Qiu. 2022 · 2022
Later among the works it cites.
D2U: distance-to-uniform learning for out-of-scope detection
Eyup Halit Yilmaz and Cagri Toraman. 2022 · 2093
Closest in time.