LingBERT, Linguistic Knowledge Injection into Attention Mechanism Based on a Hybrid Masking Strategy

In this paper, we propose lingBERT, a transformers based language model. We present two architectures of lingBERT based on hybrid masking strategy. Both models are inspired by BERT base. Our model introduces linguistic knowledge (syntactic dependencies) injection into attention mechanisms. Models like BERT employ random masking of tokens during training, which can lead to inefficiencies in capturing syntactic and semantic dependencies. To address this, our method uses two masking strategies. The first one masks words with syntactic dependencies. The second one uses a low percentage of randomly masked words. Resulted tokens from both strategies are then handed over tow proposed architectures of lingBERT. This strategy ensures that linguistic relationships are preserved and learned more effectively. Additionally, we maintain a low randomness ratio of masked tokens to avoid overfitting and enhance the model generalization. Through comprehensive experiments and evaluations, our approach demonstrates significant improvements in capturing context, leading to better performance across various NLP tasks. Furthermore, our approach affords an interpretability about our model inner workings throughout the learning process. This work offers a new direction toward knowledge injection into attention-mechanism based models, leading to advancing the capabilities of language understanding systems.