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.

The graphs in figure 1 show the distances between duplicate and non-duplicate questions using different embedding systems. Large achieved a F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates. In order to test whether these results generalised to our domain, we devised a test that would make use of what little data we had to evaluate.
How is the accuracy of the system measured?

In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 .  Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign)
Do they perform some annotation?

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 .

What is the source of memes?