  We present simple BERT-based models for relation extraction and semantic role
labeling. In recent years, state-of-the-art performance has been achieved using
neural models by incorporating lexical and syntactic features such as
part-of-speech tags and dependency trees. In this paper, extensive experiments
on datasets for these two tasks show that without using any external features,
a simple BERT-based model can achieve state-of-the-art performance. To our
knowledge, we are the first to successfully apply BERT in this manner. Our
models provide strong baselines for future research.
