In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.
Question: What data is the language model pretrained on?
[EX A]: Chinese general corpus

[EX Q]: We took inspiration from these works to design our experiments to solve the CSKS task.
Question: What problem do they apply transfer learning to?
[EX A]: CSKS task

[EX Q]: We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17 . The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks.
Question: What methods is RelNet compared to?
[EX A]:
We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17