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: BERT-style pretraining crucially relies on large quantities of text. Baevski et al. (2019) demonstrate that increasing data size can result in improved end-task performance. Several efforts have trained on datasets larger and more diverse than the original BERT Radford et al. (2019); Yang et al. (2019); Zellers et al. (2019).Unfortunately, not all of the additional datasets can be publicly released. For our study, we focus on gathering as much data as possible for experimentation, allowing us to match the overall quality and quantity of data as appropriate for each comparison. Notably You et al. (2019) train BERT with even larger batche sizes, up to 32K sequences.We leave further exploration of the limits of large batch training to future work.

Question: According to the paper, does BERT imporved by several other papers?
Yes, several other papers improved BERT. Baevski et al. (2019) prove that increasing training data size can improve performance. Moreover, (2019); Yang et al. (2019); Zellers et al. (2019) train BERT with larger and more diverse dataset than original BERT.