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International Conference on Artificial Intelligence: Methodology, Systems, and Applications

AIMSA 2018: Artificial Intelligence: Methodology, Systems, and Applications pp 25–35Cite as

Intent Detection System Based on Word Embeddings

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11089)

Abstract

Intent detection is one of the main tasks of a dialogue system. In this paper we present our intent detection system that is based on FastText word embeddings and neural network classifier. We find a significant improvement in the FastText sentence vectorization. The results show that our intent detection system provides state-of-the-art results on three English datasets outperforming many popular services.

Keywords

  • Intent detection
  • Dialog system
  • Word embeddings

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Notes

  1. 1.

    https://wit.ai/.

  2. 2.

    https://github.com/snipsco/nlu-benchmark.

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Acknowledgments

The research has been supported by the European Regional Development Fund within the project “Neural Network Modelling for Inflected Natural Languages” No. 1.1.1.1/16/A/215.

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Authors and Affiliations

  1. Tilde, Vienības gatve 75A, Riga, 1004, Latvia

    Kaspars Balodis & Daiga Deksne

  2. Faculty of Computing, University of Latvia, Raiņa blvd. 19, Riga, 1586, Latvia

    Kaspars Balodis

Authors
  1. Kaspars Balodis
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  2. Daiga Deksne
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Corresponding author

Correspondence to Kaspars Balodis .

Editor information

Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

  2. Universität des Saarlandes, Saarbrücken, Germany

    Prof. Dr. Josef van Genabith

  3. DFKI GmbH, Saarbrücken, Germany

    Thierry Declerck

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Balodis, K., Deksne, D. (2018). Intent Detection System Based on Word Embeddings. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-99344-7_3

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