Balance the Labels: Hierarchical Label Structured Network for Dialogue Act Recognition

Abstract

Existing works for Dialogue Act Recognition (DAR) pay little attention to the imbalanced distribution of the Dialogue Acts (DAs) and exclusively train their models over very fine-grained DAs in one pass, which leads to a limited performance in recognizing low-frequent DAs. To address this issue, we propose a hierarchical label structured network that explicitly introduces coarse-grained DAs to the original fine-grained DAs. A two-pass multi-head attention mechanism is devised to integrate different levels of DA information into the utterance encoding process, thereby utilizing the information learned from the coarse-grained DAs to guide the recognition of the target fine-grained DAs. Besides, a transfer learning over large-scale dialogue datasets is also employed to further boost label representation of the coarse-grained DAs. Extensive experiments show that our model significantly outperforms the state-of-the-art methods, verifying the effectiveness of integrating the hierarchical structure among DAs.