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]: Encoders with induced latent structures have been shown to benefit several tasks including document classification, natural language inference BIBREF12, BIBREF13, and machine translation BIBREF11.
[A]: Is there any evidence that encoders with latent structures work well on other tasks?

[Q]: Hate memes were retrieved from Google Images with a downloading tool. We used the following queries to collect a total of 1,695 hate memes: racist meme (643 memes), jew meme (551 memes), and muslim meme (501 Memes). Non-hate memes were obtained from the Reddit Memes Dataset .
[A]: What is the source of memes?

[Q]: In this step we extract the AMR graphs of the summary sentences using story sentence AMRs. We divide this task in two parts. First is finding the important sentences from the story and then extracting the key information from those sentences using their AMR graphs.
[A]:
How are sentences selected from the summary graph?