Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications. Previous works identify the OOD instance by leveraging a single global feature embedding to represent the sentence, which cannot characterize subtle OOD patterns well. Another major challenge current OOD methods face is learning effective low-dimensional sentence representations to identify the hard OOD instances that are semantically similar to the ID data. In this paper, we propose a new unsupervised OOD detection method, namely Semantic Role Labeling Guided Out-of-distribution Detection (SRLOOD), that separates, extracts, and learns the semantic role labeling (SRL) guided fine-grained local feature representations from different arguments of a sentence and the global feature representations of the full sentence using a margin-based contrastive loss. A novel self-supervised approach is also introduced to enhance such global-local feature learning by predicting the SRL extracted role. The resulting model achieves SOTA performance on four OOD benchmarks, indicating the effectiveness of our approach. Codes will be available upon acceptance.

本文提出了一种基于语义角色标注（SRL）的全局-局部特征学习方法SRLOOD，运用边缘对比损失，将不同参数最细的局部特征和全句的全局特征分离、提取和学习，同时引入自监督学习，该方法在四个OOD基准测试中取得了SOTA效果。

基于语义角色标注的脱离分布检测