  Recognising dialogue acts (DA) is important for many natural language
processing tasks such as dialogue generation and intention recognition. In this
paper, we propose a dual-attention hierarchical recurrent neural network for DA
classification. Our model is partially inspired by the observation that
conversational utterances are normally associated with both a DA and a topic,
where the former captures the social act and the latter describes the subject
matter. However, such a dependency between DAs and topics has not been utilised
by most existing systems for DA classification. With a novel dual task-specific
attention mechanism, our model is able, for utterances, to capture information
about both DAs and topics, as well as information about the interactions
between them. Experimental results show that by modelling topic as an auxiliary
task, our model can significantly improve DA classification, yielding better or
comparable performance to the state-of-the-art method on three public datasets.
