In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: 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.

Answer: What data is the language model pretrained on?

Question: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.

Answer: Do they perform a quantitative analysis of their model displaying knowledge distortions?

Question: Specifically, PlEWi supplied 550,755 [error, correction] pairs, from which 298,715 were unique.

Answer:
How is PIEWi annotated?