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.

Example Input: 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.
Example Output: Do they perform a quantitative analysis of their model displaying knowledge distortions?

Example Input: We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
Example Output: Is this a span-based (extractive) QA task?

Example Input: We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection
Example Output:
Why is a Gaussian process an especially appropriate method for this classification problem?