Abstract

This study investigates an approach to dialogue act classification leveraging a pre-trained model, with a specific focus on the efficacy of employing the ERNIE model for this task. Dialogue act classification is crucial for deciphering the intentions, actions, and objectives underlying conversations. In this research endeavor, we selected the ERNIE model as our pre-trained backbone, augmented it with fine-tuning techniques, and synergistically incorporated it with an RCNN architecture to achieve precise classification of dialogue acts. Through a series of experiments, we rigorously assessed the model's performance using both publicly available and proprietary datasets, comparing it with conventional methodologies and alternative deep learning frameworks. Our findings revealed that the proposed dialogue act classification methodology, anchored in the ERNIE model and RCNN integration, yielded notable improvements in accuracy and generalization capabilities. This underscores the prowess of the ERNIE model in dialogue act classification tasks, offering new insights and methodologies for analyzing dialogue text. Subsequent research avenues will delve into exploring more intricate model architectures and harnessing richer data reservoirs to further elevate the performance and applicability spectrum of dialogue act classification.