In dialogues, an utterance is a chain of consecutive sentences produced by
one speaker which ranges from a short sentence to a thousand-word post. When
studying dialogues at the utterance level, it is not uncommon that an utterance
would serve multiple functions. For instance, "Thank you. It works great."
expresses both gratitude and positive feedback in the same utterance. Multiple
dialogue acts (DA) for one utterance breeds complex dependencies across
dialogue turns. Therefore, DA recognition challenges a model's predictive power
over long utterances and complex DA context. We term this problem Concurrent
Dialogue Acts (CDA) recognition. Previous work on DA recognition either assumes
one DA per utterance or fails to realize the sequential nature of dialogues. In
this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN)
which models the interactions between utterances of long-range context. Our
model significantly outperforms existing work on CDA recognition on a tech
forum dataset.

本文主要研究了如何使用卷积 - 循环神经网络对对话中的连续性行为进行识别，解决了当前对于长篇对话和复杂对话情境下预测难度较大的问题，同时在技术论坛数据集上取得了更为优异的效果。