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.

翻译：对话意图分类旨在识别对话中用户输入的根本目的或意图。当前的意图分类系统面临显著挑战，主要源于可能的意图数量庞大以及相似意图类别之间存在显著的语义重叠。本文提出一种通过上下文学习进行少样本对话意图分类的新方法，结合动态标签精化以应对这些挑战。我们的方法从训练集中检索测试输入的相关示例，并利用大型语言模型基于语义理解动态精化意图标签，确保意图之间能够清晰区分。实验结果表明，我们的方法有效解决了语义相似意图之间的混淆问题，在多个数据集上相比基线模型显著提升了性能。我们还证明，与基线方法相比，我们的方法能够生成更具可解释性的意图标签，并在捕捉用户潜在意图方面具有更好的语义连贯性。