@inproceedings{zawbaa-etal-2024-improved,
    title = "Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification",
    author = "Zawbaa, Hossam  and
      Rashwan, Wael  and
      Dutta, Sourav  and
      Assem, Haytham",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.763",
    pages = "8708--8718",
    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.",
}

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.
Zawbaa, Hossam Rashwan, Wael Dutta, Sourav Assem, Haytham
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification