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        <title>A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification</title>
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    <name type="personal">
        <namePart type="given">Ruizhe</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Chenghua</namePart>
        <namePart type="family">Lin</namePart>
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    <name type="personal">
        <namePart type="given">Matthew</namePart>
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    <name type="personal">
        <namePart type="given">Xiao</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Guanyi</namePart>
        <namePart type="family">Chen</namePart>
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        <dateIssued>2019-11</dateIssued>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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    <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.</abstract>
    <identifier type="citekey">li-etal-2019-dual</identifier>
    <identifier type="doi">10.18653/v1/K19-1036</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
%A Li, Ruizhe
%A Lin, Chenghua
%A Collinson, Matthew
%A Li, Xiao
%A Chen, Guanyi
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-dual
%X 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.
%R 10.18653/v1/K19-1036
%U https://aclanthology.org/K19-1036
%U https://doi.org/10.18653/v1/K19-1036
%P 383-392

Markdown (Informal)

[A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification](https://aclanthology.org/K19-1036) (Li et al., CoNLL 2019)

• A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification (Li et al., CoNLL 2019)

ACL

• Ruizhe Li, Chenghua Lin, Matthew Collinson, Xiao Li, and Guanyi Chen. 2019. A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 383–392, Hong Kong, China. Association for Computational Linguistics.