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: The NSP loss was hypothesized to be an important factor in training the original BERT model. Devlin et al. (2019) observe that removing NSP hurts performance, with significant performance degradation on QNLI, MNLI, and SQuAD 1.1.However, some recent work has questioned the necessity of the NSP loss Lample and Conneau (2019); Yang et al. (2019); Joshi et al. (2019). We next compare training without the NSP loss and training with blocks of text from a single document (doc-sentences).We find that this setting outperforms the originally published BERT{}_{\textsc{base}} results and that removing the NSP loss matches or slightly improves downstream task performance, in contrast to Devlin et al. (2019).It is possible that the original BERT implementation may only have removed the loss term while still retaining the segment-pair input format.

Question: What is the difference between BERT paper and RoBERTa paper’s point of views? Give an answer in NSP loss and their performance perspective.
In BERT paper, author said that removing NSP can hurt the performance of the model. However, in RoBERTa paper, author said that removing NSP improves downstream task performance. Therefore, point of views in terms of NSP is different between BERT and RoBERTa.