Publication
EMNLP-IJCNLP 2019
Conference paper

Out-of-domain detection for low-resource text classification tasks

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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 we observe that training data is often insufficient in machine learning applications. In this work, we propose an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluation 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.

Date

03 Nov 2019

Publication

EMNLP-IJCNLP 2019

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