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 meaning of incoming utterance with the assistance of algorithm adaptation method. In order to evaluate the robustness of our proposed models, comparative experiments on the DSTC5 dialogue datasets are conducted. Experimental results show that the proposed models outperform most of the submitted models in the DSTC5 in terms of F1-score. Without any manually designed features or delexicalization, our model has proven its efficiency of tackling the multi-label SLU task, using only publicly available pre-trained word vectors. Our model is capable of retrieving the dialogue history, and thereby it could build the concise concept structure by employing the pragmatic intention as well as semantic meaning of utterances. The architecture of our semantic decoder has a potential to be applicable to other variety of human-to-human dialogues to achieve SLU.
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