Text Semantic Matching

This chapter introduces text semantic matching, a fundamental NLP task that determines semantic similarity between texts and underpins applications such as Q&A systems and information retrieval. It first discusses the limitations of early string-matching methods and highlights the advantages of neural networks and pre-trained models in capturing deeper semantic relationships. The chapter presents two approaches: SimNet, a GRU-based neural framework for short text matching using the LCQMC dataset, and RocketQA, a pre-trained model leveraging ERNIE with advanced training strategies to improve generalization. Through detailed implementation of data processing, model construction, training, and evaluation, the results demonstrate that while SimNet achieves moderate accuracy, RocketQA significantly enhances performance, underscoring the effectiveness of pre-training and attention mechanisms in semantic matching.