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FastText-Based Intent Detection for Inflected Languages

Tilde, Vienības Gatve 75A, LV-1004 Rīga, Latvia
Faculty of Computing, University of Latvia, Raiņa blvd. 19, LV-1586 Rīga, Latvia
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 18th International Conference AIMSA 2018, Varna, Bulgaria, 12–14 September 2018.
Information 2019, 10(5), 161;
Received: 15 January 2019 / Revised: 12 April 2019 / Accepted: 25 April 2019 / Published: 1 May 2019
(This article belongs to the Special Issue Artificial Intelligence—Methodology, Systems, and Applications)
PDF [856 KB, uploaded 8 May 2019]


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 a neural network classifier. We find an improvement in fastText sentence vectorization, which, in some cases, shows a significant increase in intent detection accuracy. We evaluate the system on languages commonly spoken in Baltic countries—Estonian, Latvian, Lithuanian, English, and Russian. The results show that our intent detection system provides state-of-the-art results on three previously published datasets, outperforming many popular services. In addition to this, for Latvian, we explore how the accuracy of intent detection is affected if we normalize the text in advance. View Full-Text
Keywords: intent detection; word embeddings; dialogue system intent detection; word embeddings; dialogue system

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Balodis, K.; Deksne, D. FastText-Based Intent Detection for Inflected Languages. Information 2019, 10, 161.

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