ERNIE 3.0-Based Intent Recognition

This chapter presents the implementation of an intent recognition system based on ERNIE 3.0, framing the task as a multi-label text classification problem. Using the CrossWOZ dataset, the study demonstrates how ERNIE 3.0 leverages large-scale pre-training with text and knowledge graphs to effectively capture semantic representations. The workflow covers data preprocessing, feature conversion, model construction, and training with Adam optimization and binary cross-entropy loss, evaluated using Macro F1 and Micro F1 metrics. Experimental results show that the model achieves strong performance, with stable convergence and high F1 scores on validation and test sets. The chapter concludes with prediction examples and exercises, illustrating the practical application of ERNIE 3.0 to improve the accuracy and robustness of intent recognition in real-world dialogue and search scenarios.