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        <title>Improving Spoken Language Understanding by Wisdom of Crowds</title>
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        <namePart type="given">Koichiro</namePart>
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    <name type="personal">
        <namePart type="given">Katsuhito</namePart>
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    <name type="personal">
        <namePart type="given">Satoshi</namePart>
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    <abstract>Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task. The lack of training data is an important problem, especially for new system tasks, because existing SLU systems are based on statistical approaches. In this paper, we proposed to use two sources of the “wisdom of crowds,” crowdsourcing and knowledge community website, for improving the SLU system. We firstly collected paraphrasing variations for new system tasks through crowdsourcing as seed data, and then augmented them using similar questions from a knowledge community website. We investigated the effects of the proposed data augmentation method in SLU task, even with small seed data. In particular, the proposed architecture augmented more than 120,000 samples to improve SLU accuracies.</abstract>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Improving Spoken Language Understanding by Wisdom of Crowds
%A Yoshino, Koichiro
%A Ikeuchi, Kana
%A Sudoh, Katsuhito
%A Nakamura, Satoshi
%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 yoshino-etal-2020-improving
%X Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task. The lack of training data is an important problem, especially for new system tasks, because existing SLU systems are based on statistical approaches. In this paper, we proposed to use two sources of the “wisdom of crowds,” crowdsourcing and knowledge community website, for improving the SLU system. We firstly collected paraphrasing variations for new system tasks through crowdsourcing as seed data, and then augmented them using similar questions from a knowledge community website. We investigated the effects of the proposed data augmentation method in SLU task, even with small seed data. In particular, the proposed architecture augmented more than 120,000 samples to improve SLU accuracies.
%R 10.18653/v1/2020.coling-main.234
%U https://aclanthology.org/2020.coling-main.234
%U https://doi.org/10.18653/v1/2020.coling-main.234
%P 2606-2612

Markdown (Informal)

[Improving Spoken Language Understanding by Wisdom of Crowds](https://aclanthology.org/2020.coling-main.234) (Yoshino et al., COLING 2020)

• Improving Spoken Language Understanding by Wisdom of Crowds (Yoshino et al., COLING 2020)

ACL

• Koichiro Yoshino, Kana Ikeuchi, Katsuhito Sudoh, and Satoshi Nakamura. 2020. Improving Spoken Language Understanding by Wisdom of Crowds. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2606–2612, Barcelona, Spain (Online). International Committee on Computational Linguistics.