March 14, 2024, 4:48 a.m. | Louis Owen, Biddwan Ahmed, Abhay Kumar

cs.CL updates on arXiv.org arxiv.org

arXiv:2306.08852v2 Announce Type: replace
Abstract: 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, …

arxiv cs.cl detection distribution encoder type

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