@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://preview.aclanthology.org/ingest_wac_2008/2021.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."
}