Exploring Machine Learning and Deep Learning Frameworks for Task-Oriented Dialogue Act Classification

Dialogue Act (DA) Classification plays a signifi-cant role in the understanding of an utterance in a dialogue. Components of Spoken Dialogue System (SDS) such as Natural Language Understanding (NLU) and Dialogue Management (DM) modules can significantly exploit the output of the DA classification. In this paper, we propose a task-oriented DA classifier based on both traditional supervised Machine Learning (ML) as well as Deep Learning (DL) techniques. The type and nature of dialogues basically depend on the domain and the DA itself. So, in order to make the model task-oriented, a new tag-set has been designed by studying the properties of the target domain. On the benchmark SwitchBoard (SWBD) and TRAINS corpus, our proposed models have performed exceptionally well with the new tag-set. Experimental results indicate that our proposed models have achieved good accuracy on both the datasets and outperformed several state of the art approaches and the new tag-set is well suited for task-oriented applications.

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