@inproceedings{shu-etal-2021-odist-open,
title = "{ODIST}: Open World Classification via Distributionally Shifted Instances",
author = "Shu, Lei and
Benajiba, Yassine and
Mansour, Saab and
Zhang, Yi",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.316",
doi = "10.18653/v1/2021.findings-emnlp.316",
pages = "3751--3756",
abstract = "In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances. This novel and straightforward method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.",
}
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<abstract>In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances. This novel and straightforward method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.</abstract>
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%0 Conference Proceedings
%T ODIST: Open World Classification via Distributionally Shifted Instances
%A Shu, Lei
%A Benajiba, Yassine
%A Mansour, Saab
%A Zhang, Yi
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F shu-etal-2021-odist-open
%X In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances. This novel and straightforward method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.
%R 10.18653/v1/2021.findings-emnlp.316
%U https://aclanthology.org/2021.findings-emnlp.316
%U https://doi.org/10.18653/v1/2021.findings-emnlp.316
%P 3751-3756
Markdown (Informal)
[ODIST: Open World Classification via Distributionally Shifted Instances](https://aclanthology.org/2021.findings-emnlp.316) (Shu et al., Findings 2021)
ACL