Multi-perspective Enhancement Of Text Semantic Matching

With the widespread adoption of social media, text matching tasks have gained a prominent role in the field of natural language processing. However, traditional text matching methods often overly rely on shallow features and do not adequately consider the contextual semantic information within the text. With the rise of deep neural networks, deep learning methods have ushered in new directions in the field of text matching, enabling us to better understand and process semantic information within text. We introduce a framework that combines various models and techniques to more accurately assess semantic similarity between Chinese texts. Within this framework, we integrate the contextual understanding capability of BERT with neural network architectures such as Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and self-attention mechanisms. Through the fusion of multi-perspective information, we are better equipped to handle semantic ambiguities and polysemy in text matching tasks, ultimately improving matching performance. Extensive experimental evaluations were conducted on publicly available Chinese datasets, BQ and LCQMC. The results demonstrate that our multi-perspective textual semantic matching approach has significantly outperformed existing methods in terms of performance.