A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
October 22, 2018 ยท Declared Dead ยท ๐ Conference on Computational Natural Language Learning
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Authors
Ruizhe Li, Chenghua Lin, Matthew Collinson, Xiao Li, Guanyi Chen
arXiv ID
1810.09154
Category
cs.CL: Computation & Language
Citations
57
Venue
Conference on Computational Natural Language Learning
Last Checked
4 months ago
Abstract
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.
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