End-to-end convolutional neural networks for intent detection

Convolutional Neural Networks (CNNs) have been applied to various machine learning tasks, such as computer vision, speech technologies and machine translation. One of the main advantages of CNNs is the representation learning capability from highdimensional 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

End-to-end Convolutional Neural Networks for Intent Detection

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

End-to-end Convolutional Neural Networks for Intent Detection

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