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7 August 2024 Dialogue act classification based on pre-trained model
Xiaoling Xia, Yingying Xu
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Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292Y (2024) https://doi.org/10.1117/12.3038221
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xiaoling Xia and Yingying Xu "Dialogue act classification based on pre-trained model", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292Y (7 August 2024); https://doi.org/10.1117/12.3038221
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KEYWORDS
Data modeling
Deep learning
Feature extraction
Performance modeling
Classification systems
Neural networks
Transformers
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