  Detecting out-of-scope user utterances is essential for task-oriented
dialogues and intent classification. Current methodologies face difficulties
with the unpredictable distribution of outliers and often rely on assumptions
about data distributions. We present the Dual Encoder for Threshold-Based
Re-Classification (DETER) to address these challenges. This end-to-end
framework efficiently detects out-of-scope intents without requiring
assumptions on data distributions or additional post-processing steps. The core
of DETER utilizes dual text encoders, the Universal Sentence Encoder (USE) and
the Transformer-based Denoising AutoEncoder (TSDAE), to generate user utterance
embeddings, which are classified through a branched neural architecture.
Further, DETER generates synthetic outliers using self-supervision and
incorporates out-of-scope phrases from open-domain datasets. This approach
ensures a comprehensive training set for out-of-scope detection. Additionally,
a threshold-based re-classification mechanism refines the model's initial
predictions. Evaluations on the CLINC-150, Stackoverflow, and Banking77
datasets demonstrate DETER's efficacy. Our model outperforms previous
benchmarks, increasing up to 13% and 5% in F1 score for known and unknown
intents on CLINC-150 and Stackoverflow, and 16% for known and 24% % for unknown
intents on Banking77. The source code has been released at
https://github.com/Hossam-Mohammed-tech/Intent_Classification_OOS.
