Out-of-Domain Detection for Low-Resource Text Classification Tasks
Ming Tan, Yang Yu, Haoyu Wang, Dakuo Wang, Saloni Potdar, Shiyu Chang, Mo Yu
Abstract
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since in machine learning applications we observe that training data is often insufficient. In this work, we propose an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluations on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.- Anthology ID:
- D19-1364
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3566–3572
- Language:
- URL:
- https://aclanthology.org/D19-1364
- DOI:
- 10.18653/v1/D19-1364
- Cite (ACL):
- Ming Tan, Yang Yu, Haoyu Wang, Dakuo Wang, Saloni Potdar, Shiyu Chang, and Mo Yu. 2019. Out-of-Domain Detection for Low-Resource Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3566–3572, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Out-of-Domain Detection for Low-Resource Text Classification Tasks (Tan et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/D19-1364.pdf
- Code
- SLAD-ml/few-shot-ood