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        <title>Unsupervised dialogue intent detection via hierarchical topic model</title>
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        <namePart type="given">Artem</namePart>
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        <namePart type="given">Victor</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Eugenia</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>One of the challenges during a task-oriented chatbot development is the scarce availability of the labeled training data. The best way of getting one is to ask the assessors to tag each dialogue according to its intent. Unfortunately, performing labeling without any provisional collection structure is difficult since the very notion of the intent is ill-defined. In this paper, we propose a hierarchical multimodal regularized topic model to obtain a first approximation of the intent set. Our rationale for hierarchical models usage is their ability to take into account several degrees of the dialogues relevancy. We attempt to build a model that can distinguish between subject-based (e.g. medicine and transport topics) and action-based (e.g. filing of an application and tracking application status) similarities. In order to achieve this, we divide set of all features into several groups according to part-of-speech analysis. Various feature groups are treated differently on different hierarchy levels.</abstract>
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%0 Conference Proceedings
%T Unsupervised dialogue intent detection via hierarchical topic model
%A Popov, Artem
%A Bulatov, Victor
%A Polyudova, Darya
%A Veselova, Eugenia
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F popov-etal-2019-unsupervised
%X One of the challenges during a task-oriented chatbot development is the scarce availability of the labeled training data. The best way of getting one is to ask the assessors to tag each dialogue according to its intent. Unfortunately, performing labeling without any provisional collection structure is difficult since the very notion of the intent is ill-defined. In this paper, we propose a hierarchical multimodal regularized topic model to obtain a first approximation of the intent set. Our rationale for hierarchical models usage is their ability to take into account several degrees of the dialogues relevancy. We attempt to build a model that can distinguish between subject-based (e.g. medicine and transport topics) and action-based (e.g. filing of an application and tracking application status) similarities. In order to achieve this, we divide set of all features into several groups according to part-of-speech analysis. Various feature groups are treated differently on different hierarchy levels.
%R 10.26615/978-954-452-056-4_108
%U https://aclanthology.org/R19-1108
%U https://doi.org/10.26615/978-954-452-056-4_108
%P 932-938

Markdown (Informal)

[Unsupervised dialogue intent detection via hierarchical topic model](https://aclanthology.org/R19-1108) (Popov et al., RANLP 2019)

• Unsupervised dialogue intent detection via hierarchical topic model (Popov et al., RANLP 2019)

ACL

• Artem Popov, Victor Bulatov, Darya Polyudova, and Eugenia Veselova. 2019. Unsupervised dialogue intent detection via hierarchical topic model. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 932–938, Varna, Bulgaria. INCOMA Ltd..