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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Abstract

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.

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