Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …

Abstract Out-of-distribution (OOD) detection is indispensable for machine learning models
deployed in the open world. Recently, the use of an auxiliary outlier dataset during training …

Abstract The Out-of-Domain (OOD) intent classification is a basic and challenging task for
dialogue systems. Previous methods commonly restrict the region (in feature space) of In …

Intent recognition is a crucial task in natural language understanding. Current research
mainly focuses on task-specific unimodal intent recognition. However, in real-world scenes …

Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning.
Recent multi-modal OOD detection leverages textual information from in-distribution (ID) …

Open-set domain adaptation (OSDA) aims to achieve knowledge transfer in the presence of
both domain shift and label shift, which assumes that there exist additional unknown target …

Out-of-distribution (OOD) detection is a critical task for reliable predictions over text. Fine-
tuning with pre-trained language models has been a de facto procedure to derive OOD …

Open intent detection is a critical task within dialogue systems, aiming to effectively classify
known intents while also identifying unknown intents that have not been encountered in the …

Abstract Out-of-domain (OOD) intent classification is an active field of natural language
understanding, which is of great practical significance for intelligent devices such as the …

Pre-trained Transformer-based models were reported to be robust in intent classification. In
this work, we first point out the importance of in-domain out-of-scope detection in few-shot …