EMNLP 2021finding24 citations
ODIST: Open World Classification via Distributionally Shifted Instances
Lei Shu, Yassine Benajiba, Saab Mansour, Yi Zhang
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.
BibTeX
@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"
}