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

Hossam Zawbaa, Wael Rashwan, Sourav Dutta, Haytham Assem


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, achieving an increase of 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.
Anthology ID:
2024.lrec-main.763
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
8708–8718
Language:
URL:
https://aclanthology.org/2024.lrec-main.763
DOI:
Bibkey:
Cite (ACL):
Hossam Zawbaa, Wael Rashwan, Sourav Dutta, and Haytham Assem. 2024. Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8708–8718, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification (Zawbaa et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.763.pdf