Out-of-Domain Detection for Low-Resource Text Classification Tasks
It addresses a realistic but understudied problem of detecting out-of-domain cases with limited training data, which is incremental as it builds on existing methods for low-resource settings.
The paper tackled out-of-domain detection for low-resource text classification by proposing an OOD-resistant Prototypical Network, which outperformed state-of-the-art methods in zero-shot OOD detection and maintained competitive in-domain classification performance.
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.