An Exploration of Dialog Act Classification in Open-domain Conversational Agents and the Applicability of Text Data Augmentation

Abstract

Recognizing dialog acts of users is an essential component in building successful conversational agents. In this work, we propose a dialog act (DA) classifier for two of our open domain conversational agents. For this, we curated a high-quality, multi-domain dataset with ∼24k user utterances labelled into 8 suitable DAs. Our fine-tuned BERT-based model outperforms the baseline SVM classifier by achieving state-of-the-art accuracy on the proposed dataset. Moreover, it generalizes well on unseen data. To address the issue of data scarcity when training DA classifiers, we implemented different data augmentation techniques and compared their performance. Our extensive experiments show that, in a simulated low data regime with only 10 examples per label, methods as simple as synonym replacement can double the size of the existing training data and boost accuracy of our DA classifier by ∼8%. Lastly, we demonstrate how our proposed classifier and augmentation techniques can be adapted to effectively detect dialog acts in languages other than English.