This paper introduces a novel method leveraging bi-encoder-based detectors
along with a comprehensive study comparing different out-of-distribution (OOD)
detection methods in NLP using different feature extractors. The feature
extraction stage employs popular methods such as Universal Sentence Encoder
(USE), BERT, MPNET, and GLOVE to extract informative representations from
textual data. The evaluation is conducted on several datasets, including
CLINC150, ROSTD-Coarse, SNIPS, and YELLOW. Performance is assessed using
metrics such as F1-Score, MCC, FPR@90, FPR@95, AUPR, an AUROC. The experimental
results demonstrate that the proposed bi-encoder-based detectors outperform
other methods, both those that require OOD labels in training and those that do
not, across all datasets, showing great potential for OOD detection in NLP. The
simplicity of the training process and the superior detection performance make
them applicable to real-world scenarios. The presented methods and benchmarking
metrics serve as a valuable resource for future research in OOD detection,
enabling further advancements in this field. The code and implementation
details can be found on our GitHub repository:
this https URL

本文介绍了一种新的方法，利用双编码器检测器，并通过比较不同的特征提取器在自然语言处理（NLP）中的不同外域检测方法进行全面研究。实验结果表明，该方法在所有数据集上都优于其他方法，具有很大的潜力应用于 NLP 领域的 OOD 检测中。