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        <title>Two-level classification for dialogue act recognition in task-oriented dialogues</title>
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    <name type="personal">
        <namePart type="given">Philippe</namePart>
        <namePart type="family">Blache</namePart>
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    <name type="personal">
        <namePart type="given">Massina</namePart>
        <namePart type="family">Abderrahmane</namePart>
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    <name type="personal">
        <namePart type="given">Stéphane</namePart>
        <namePart type="family">Rauzy</namePart>
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    <name type="personal">
        <namePart type="given">Magalie</namePart>
        <namePart type="family">Ochs</namePart>
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    <name type="personal">
        <namePart type="given">Houda</namePart>
        <namePart type="family">Oufaida</namePart>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
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    <abstract>Dialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA). This approach makes it possible to benefit from the very good accuracy of generic DA classification at the first level and proposes an efficient approach for specific DA, based on high-level linguistic features. Our results show the interest of involving such features into the classifiers, outperforming all other feature sets, in particular those classically used in DA classification.</abstract>
    <identifier type="citekey">blache-etal-2020-two</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.431</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Two-level classification for dialogue act recognition in task-oriented dialogues
%A Blache, Philippe
%A Abderrahmane, Massina
%A Rauzy, Stéphane
%A Ochs, Magalie
%A Oufaida, Houda
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F blache-etal-2020-two
%X Dialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA). This approach makes it possible to benefit from the very good accuracy of generic DA classification at the first level and proposes an efficient approach for specific DA, based on high-level linguistic features. Our results show the interest of involving such features into the classifiers, outperforming all other feature sets, in particular those classically used in DA classification.
%R 10.18653/v1/2020.coling-main.431
%U https://aclanthology.org/2020.coling-main.431
%U https://doi.org/10.18653/v1/2020.coling-main.431
%P 4915-4925

Markdown (Informal)

[Two-level classification for dialogue act recognition in task-oriented dialogues](https://aclanthology.org/2020.coling-main.431) (Blache et al., COLING 2020)

• Two-level classification for dialogue act recognition in task-oriented dialogues (Blache et al., COLING 2020)

ACL

• Philippe Blache, Massina Abderrahmane, Stéphane Rauzy, Magalie Ochs, and Houda Oufaida. 2020. Two-level classification for dialogue act recognition in task-oriented dialogues. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4915–4925, Barcelona, Spain (Online). International Committee on Computational Linguistics.