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.- Anthology ID:
- 2021.findings-emnlp.316
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3751–3756
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.316
- DOI:
- 10.18653/v1/2021.findings-emnlp.316
- Cite (ACL):
- Lei Shu, Yassine Benajiba, Saab Mansour, and Yi Zhang. 2021. ODIST: Open World Classification via Distributionally Shifted Instances. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3751–3756, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- ODIST: Open World Classification via Distributionally Shifted Instances (Shu et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.findings-emnlp.316.pdf
- Data
- MultiNLI