End-to-end Convolutional Neural Networks for Intent Detection

Yolchuyeva, Sevinj and Németh, Géza and Gyires-Tóth, Bálint (2019) End-to-end Convolutional Neural Networks for Intent Detection. In: XV. Magyar Számítógépes Nyelvészeti Konferencia, 2019 január 24-25, Szeged, Magyarország.


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Convolutional Neural Networks (CNNs) have been applied to various machine learn-ing tasks, such as computer vision, speech technologies and machine translation. One of the main advantages of CNNs is the representation learning capability from high-dimensional data. End-to-end CNN models have been massively explored in computer vision domain, and this approach has also been attempted in other domains as well. In this paper, a novel end-to-end CNN architecture with residual connections is presented for intent detection, which is one of the main goals for building a spoken language understanding (SLU) system. Experiments on two datasets (ATIS and Snips) were carried out. The results demonstrate that the proposed model outperforms previous solutions.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában
Depositing User: Dr. Gyires-Tóth Bálint Pál
Date Deposited: 25 Sep 2019 21:02
Last Modified: 25 Sep 2019 21:02

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