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        <title>ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents</title>
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        <namePart type="given">Stefano</namePart>
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        <namePart type="given">Alessandra</namePart>
        <namePart type="family">Cervone</namePart>
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    <name type="personal">
        <namePart type="given">Evgeny</namePart>
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    <name type="personal">
        <namePart type="given">Giuliano</namePart>
        <namePart type="family">Tortoreto</namePart>
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    <name type="personal">
        <namePart type="given">Giuseppe</namePart>
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    <abstract>Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers’ intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.</abstract>
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        <url>https://aclanthology.org/C18-1300</url>
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        <date>2018-aug</date>
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%0 Conference Proceedings
%T ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents
%A Mezza, Stefano
%A Cervone, Alessandra
%A Stepanov, Evgeny
%A Tortoreto, Giuliano
%A Riccardi, Giuseppe
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F mezza-etal-2018-iso
%X Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers’ intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.
%U https://aclanthology.org/C18-1300
%P 3539-3551

Markdown (Informal)

[ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents](https://aclanthology.org/C18-1300) (Mezza et al., COLING 2018)

• ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents (Mezza et al., COLING 2018)

ACL

• Stefano Mezza, Alessandra Cervone, Evgeny Stepanov, Giuliano Tortoreto, and Giuseppe Riccardi. 2018. ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3539–3551, Santa Fe, New Mexico, USA. Association for Computational Linguistics.