Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification

• URL: http://arxiv.org/abs/2405.19967v2
• Date: Fri, 31 May 2024 08:54:24 GMT
• Title: Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification
• Authors: Hossam M. Zawbaa, Wael Rashwan, Sourav Dutta, Haytham Assem,
• Abstract summary: Current methodologies face difficulties with the unpredictable distribution of outliers. We present the Dual for Threshold-Based Re-Classification (DETER) to address these challenges. Our model outperforms previous benchmarks, increasing up to 13% and 5% in F1 score for known and unknown intents.
• Score: 6.975902383951604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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