用外部知识增强预训练模型，提升语义匹配效果
Using External knowledge to Enhanced PLM for Semantic Matching
- 引入外部知识增强预训练语义匹配模型
- 在10个公开数据集上均优于基线模型
- 适合需要精准语义理解的NLP任务
语义相关性建模一直是自然语言处理中的关键挑战。近年来，随着大量标注数据的出现，基于神经网络的推理模型得以训练，展现出优异的实际应用性能，并达到当前最佳水平。然而，即使拥有大规模标注数据，我们仍需思考：机器能否仅靠这些数据学习到完成语义相关性检测所需的全部知识？若不能，如何将外部知识融入神经网络模型？如何构建能充分利用外部知识的相关性检测模型？本文通过引入外部知识来增强预训练的语义相关性判别模型。在10个公开数据集上的实验结果表明，该方法相较于基线模型实现了持续性能提升。
原文摘要 · Abstract (English)
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.
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