Exploring Label Hierarchy in Dialogue Intent Classification

Abstract

Dialogue intent classification is a pivotal task in natural language understanding, crucial for effective human-computer interactions. Despite significant progress in structural modeling of dialogues and texts, existing research still has several limitations: intention labels are treated as independent entities, ignoring the hierarchical relationships and dependencies among them and making it hard to make accurate predictions on fine-grained labels. In this paper, we propose a Hierarchical Label-aware Dialogue Intent Classification model (HLDIC) for dialogue intent classification. Specifically, we leverage the hierarchical relationships of labels by introducing a coarse-grained label classification auxiliary task. A hierarchical adaptive attention mechanism is proposed, which employs gate mechanisms to guide the model in recognizing keywords and vital adjacent pairs. To further explore label dependencies, a hierarchy-aware mechanism is proposed to use a mask matrix to allow the model to focus on the correct fine-grained labels within the corresponding coarse-grained labels and partially suppress the noise from other coarse-grained labels. Experimental results on public CCL2018-Task1 corpus show the superior performance of HLDIC.