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.

Q: While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description.

A: How do you find the entity descriptions?
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Q: A total of 27 different genres were scraped.

A: how many movie genres do they explore?
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Q: In this work, we use pretraining on the unlabeled data of each task and show that it can increase the performance of classification systems.

A:
Where is MVCNN pertained?
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