@inproceedings{tan-etal-2019-domain,
title = "Out-of-Domain Detection for Low-Resource Text Classification Tasks",
author = "Tan, Ming and
Yu, Yang and
Wang, Haoyu and
Wang, Dakuo and
Potdar, Saloni and
Chang, Shiyu and
Yu, Mo",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1364",
doi = "10.18653/v1/D19-1364",
pages = "3566--3572",
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 \textit{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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Out-of-Domain Detection for Low-Resource Text Classification Tasks
%A Tan, Ming
%A Yu, Yang
%A Wang, Haoyu
%A Wang, Dakuo
%A Potdar, Saloni
%A Chang, Shiyu
%A Yu, Mo
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F tan-etal-2019-domain
%X 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.
%R 10.18653/v1/D19-1364
%U https://aclanthology.org/D19-1364
%U https://doi.org/10.18653/v1/D19-1364
%P 3566-3572
Markdown (Informal)
[Out-of-Domain Detection for Low-Resource Text Classification Tasks](https://aclanthology.org/D19-1364) (Tan et al., EMNLP-IJCNLP 2019)
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.