Convolutional Neural Network using a Threshold Predictor for Multi-label Speech Act Classification for the 5th Dialogue State Tracking Challenge

초록

Regarding the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5), it is required to classify label sets of speech acts on human-to-human dialogues. In this paper, we propose a multi-label classification model with the assistance of algorithm adaptation method. To be specific, a Convolutional Neural Network (CNN) model on top of pre-trained word vectors is adapted for the multi-label classification task by utilizing a threshold learning mechanism. In order to evaluate the performance of our proposed model, comparative experiments on the DSTC5 dialogue datasets are conducted. Experimental results show that the proposed model outperforms most of the submitted model in the DSTC5 in terms of F1-score. Without any manually designed features, our model has advantage of handling the multi-label SLU task, using only publicly available pre-trained word vectors.

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