K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining
This work addresses the limitation of implicit semantics in pre-trained language models for NLP applications, though it appears incremental as it builds on existing transformer architectures.
The authors tackled the problem of language models relying only on surface information by proposing a method to incorporate explicit knowledge from knowledge graphs into transformer pretraining, which improved performance on various NLP tasks.
Though pre-trained language models such as Bert and XLNet, have rapidly advanced the state-of-the-art on many NLP tasks, they implicit semantics only relying on surface information between words in corpus. Intuitively, background knowledge influences the efficacy of understanding. Inspired by this common sense, we focus on improving model pretraining by leveraging explicit knowledge. Different from recent research that optimize pretraining model by knowledge masking strategies, we propose a simple but general method to combine explicit knowledge with pretraining. To be specific, we first match knowledge facts from knowledge graph (KG) and then add a knowledge injunction layer to transformer directly without changing its architecture. The present study seeks to find the direct impact of explicit knowledge on transformer per-training. We conduct experiments on various datasets for different downstream tasks. The experimental results show that solely by adding external knowledge to transformer can improve the learning performance on many NLP tasks.