Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification
Abstract: Dialogue intent classification aims to identify the underlying purpose or intent of a user’s input in a conversation. Current intent classification systems encounter considerable challenges, primarily due to the vast number of possible intents and the significant semantic
overlap among similar intent classes. In this paper, we propose a novel approach to few-shot dialogue intent classification through in context learning, incorporating dynamic label refinement to address these challenges. Our method retrieves relevant examples for a test
input from the training set and leverages a large language model to dynamically refine intent labels based on semantic understanding, ensuring that intents are clearly distinguishable from one another. Experimental results demonstrate that our approach effectively resolves confusion between semantically similar intents, resulting in significantly enhanced performance across multiple datasets compared to baselines. We also show that our method generates more interpretable intent labels, and has a better semantic coherence in capturing underlying user intents compared to baselines.
Paper Type: Short
Research Area: Machine Learning for NLP
Research Area Keywords: Few-Shot Learning, Intent Classification, Retrieval-Based
Contribution Types: NLP engineering experiment, Data analysis
Languages Studied: English
Submission Number: 1900
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