Using External knowledge to Enhanced PLM for Semantic Matching

TL;DR

This paper explores how external knowledge can be integrated into pre-trained models to improve semantic relevance detection, demonstrating consistent performance gains across multiple datasets.

Contribution

It introduces a method for incorporating external knowledge into PLMs for semantic matching, enhancing their effectiveness beyond relying solely on annotated data.

Findings

Improved accuracy on 10 public datasets

Consistent performance enhancement over baseline models

Effective utilization of external knowledge sources

Abstract

Modeling semantic relevance has always been a challenging and critical task in natural language processing. In recent years, with the emergence of massive amounts of annotated data, it has become feasible to train complex models, such as neural network-based reasoning models. These models have shown excellent performance in practical applications and have achieved the current state-ofthe-art performance. However, even with such large-scale annotated data, we still need to think: Can machines learn all the knowledge necessary to perform semantic relevance detection tasks based on this data alone? If not, how can neural network-based models incorporate external knowledge into themselves, and how can relevance detection models be constructed to make full use of external knowledge? In this paper, we use external knowledge to enhance the pre-trained semantic relevance discrimination model. Experimental results on 10 public datasets show that our method achieves consistent improvements in performance compared to the baseline model.