You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Recent work on sentence-level RE can be divided into two lines.One focuses on injecting external knowledge into PLMs.Methods of such, including ERNIE Zhang et al. (2019) and KnowBERT Peters et al. (2019), take entity embedding pretrained from knowledge graphs as inputs to the Transformer.Similarly, K-Adapter Wang et al. (2020) introduces a plug-in neural adaptor that injects factual and linguistic knowledge into the language model.LUKE Yamada et al. (2020) further extends the pretraining objective of masked language modeling to entities and proposes an entity-aware self-attention mechanism.The other line of work focuses on continually pretraining PLMs on text with linked entities using relation-oriented objectives.Specifically, BERT-MTB Baldini Soares et al. (2019) proposes a matching-the-blanks objective that decides whether two relation instances share the same entities.Despite extensively studied, existing RE models still perform far from perfect.On the commonly-used benchmark TACRED Zhang et al. (2017), the SOTA F_{1} result only increases from 70.1\% (BERT{}_{\text{LARGE}}) to 72.7\% (LUKE) after applying PLMs to this task.It is unclear what building block is missing to constitute a promising RE system.

Question: Is there any different way to construct RE model instead of using PLM strategy previously?
Yes, there are several works of constructing RE model. ERNIE Zhang et al. (2019) and KnowBERT Peters et al. (2019) construct RE model by injecting external knowledge into PLMs. BERT-MTB Baldini Soares et al. (2019) continually pretrain PLMs on text with linked entities using relation-oriented objectives.