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        <title>Few-shot Pseudo-Labeling for Intent Detection</title>
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        <namePart type="given">Thomas</namePart>
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        <namePart type="given">Christophe</namePart>
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    <name type="personal">
        <namePart type="given">Julien</namePart>
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    <name type="personal">
        <namePart type="given">Wilfried</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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            <namePart type="given">Chengqing</namePart>
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    <abstract>In this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection. We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. We show that our two-step method yields significant improvement over existing solutions. This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced. Moreover, we confirm this results on the more general text classification task. We also demonstrate that our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.</abstract>
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%0 Conference Proceedings
%T Few-shot Pseudo-Labeling for Intent Detection
%A Dopierre, Thomas
%A Gravier, Christophe
%A Subercaze, Julien
%A Logerais, Wilfried
%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 dopierre-etal-2020-shot
%X In this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection. We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. We show that our two-step method yields significant improvement over existing solutions. This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced. Moreover, we confirm this results on the more general text classification task. We also demonstrate that our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.
%R 10.18653/v1/2020.coling-main.438
%U https://aclanthology.org/2020.coling-main.438/
%U https://doi.org/10.18653/v1/2020.coling-main.438
%P 4993-5003

Markdown (Informal)

[Few-shot Pseudo-Labeling for Intent Detection](https://aclanthology.org/2020.coling-main.438/) (Dopierre et al., COLING 2020)

• Few-shot Pseudo-Labeling for Intent Detection (Dopierre et al., COLING 2020)

ACL

• Thomas Dopierre, Christophe Gravier, Julien Subercaze, and Wilfried Logerais. 2020. Few-shot Pseudo-Labeling for Intent Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4993–5003, Barcelona, Spain (Online). International Committee on Computational Linguistics.