Dialogue act classification (DAC) is a critical task for spoken language
understanding in dialogue systems. Prosodic features such as energy and pitch
have been shown to be useful for DAC. Despite their importance, little research
has explored neural approaches to integrate prosodic features into end-to-end
(E2E) DAC models which infer dialogue acts directly from audio signals. In this
work, we propose an E2E neural architecture that takes into account the need
for characterizing prosodic phenomena co-occurring at different levels inside
an utterance. A novel part of this architecture is a learnable gating mechanism
that assesses the importance of prosodic features and selectively retains core
information necessary for E2E DAC. Our proposed model improves DAC accuracy by
1.07% absolute across three publicly available benchmark datasets.

本文提出一种端到端神经网络架构，考虑到在话语中不同层级中共现的韵律现象的表征需求并利用一个可学习的门控机制来评估韵律特征的重要性并选择性地保留端对端 DAC 所需的核心信息。实验结果表明，我们的模型在三个公共基准数据集上将 DAC 的精度提高了 1.07% 的绝对值。