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Keyword: Spoken Language Understanding (SLU)Open AccessArticle
8 Pages, 1,842 KB 
Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data
Abstract:
One of the open domain challenges for Spoken Dialogue System (SDS) is to maintain a natural conversation for rarely visited domain i.e. domain with fewer data. Spoken Language Understanding (SLU) is a component of SDS that converts user utterance into a semantic form that a computer can understand. If we scale SDS open domain challenge…
Read More(This article belongs to the SP9 (Special Issue on Multidisciplinary Innovation in Engineering Science & Technology 2020) & Section Interdisciplinary Applications of Computer Science (CSI))
Open AccessArticle
7 Pages, 1,135 KB
Retrieving Dialogue History in Deep Neural Networks for Spoken Language Understanding
Abstract:
In this paper, we propose a revised version of the semantic decoder for multi-label classification task in the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5). Our model concatenates two deep neural networks – a Convolutional Neural Network (CNN) and a Recurrent Neural Networks (RNN) – for detecting semantic…
Read More(This article belongs to the SP3 (Special issue on Recent Advances in Engineering Systems 2017) & Section Interdisciplinary Applications of Computer Science (CSI))