Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2024
Abstract
The paper introduces Semantics of Class Labelbased Unsupervised Out of Scope Intent Detection (SCOOS), a novel method aimed at enhancing out-of-scope (OOS) intent classification in task-oriented dialogue systems. Unlike prior approaches that rely solely on indomain (ID) data features, SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. The method entails forming a compact feature space centered around the semantics of class labels by minimizing losses between ID features and class names. SCOOS achieves this by creating a compact feature space centered around class label semantics, achieved through minimizing losses between in-domain (ID) features and class names. This involves training two spherical variational autoencoders concurrently to learn a shared latent space between ID features and class names, aligning ID feature data based on the corresponding classes in the latent space, and training a classifier for (m + 1)-class classification using only ID samples, where the (m+1)th class represents OOS samples. Extensive evaluation of three datasets demonstrates that SCOOS outperforms existing methods not only for OOS intent detection but also for ID intent classification. Additionally, an ablation study is conducted to analyze the impact of different components of SCOOS, and we also presented the visualization of the latent space representation providing insights into the influence of semantic information from class labels.
Keywords
Out-of-scope intent classification, Dialogue systems, Class label semantics, Out-of-scope intent detection
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 19th Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) : Miami, Florida, USA, November 12-16
First Page
9100
Last Page
9112
Identifier
10.18653/v1/2024.findings-emnlp.531
Publisher
Association for Computational Linguistics
City or Country
Miami, Florida
Citation
GAUTAM, Chandan; PARAMESWARAN, Sethupathy; KANE, Aditya; FANG, Yuan; RAMASAMY, Savitha; SUNDARAM, Suresh; SAHU, Sunil Kumar; and LI, Xiaoli.
Class name guided out-of-scope intent classification. (2024). Proceedings of the 19th Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) : Miami, Florida, USA, November 12-16. 9100-9112.
Available at: https://ink.library.smu.edu.sg/sis_research/9752
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/2024.findings-emnlp.531
Comments
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