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MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base

2022-07-28 07:39:30
Hui Li, Xuekang Yang, Xin Zhao, Lin Yu, Jiping Zheng, Wei Sun

Abstract

Incorporating prior knowledge into pre-trained language models has proven to be effective for knowledge-driven NLP tasks, such as entity typing and relation extraction. Current pre-training procedures usually inject external knowledge into models by using knowledge masking, knowledge fusion and knowledge replacement. However, factual information contained in the input sentences have not been fully mined, and the external knowledge for injecting have not been strictly checked. As a result, the context information cannot be fully exploited and extra noise will be introduced or the amount of knowledge injected is limited. To address these issues, we propose MLRIP, which modifies the knowledge masking strategies proposed by ERNIE-Baidu, and introduce a two-stage entity replacement strategy. Extensive experiments with comprehensive analyses illustrate the superiority of MLRIP over BERT-based models in military knowledge-driven NLP tasks.

Abstract (translated)

URL

https://arxiv.org/abs/2207.13929

PDF

https://arxiv.org/pdf/2207.13929.pdf


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