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.

We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).
Did they use a relation extraction method to construct the edges in the graph?

In this work, we build a novel regression model, based on linguistic, content, behavioral and topic features to detect Arabic Twitter bots to understand the impact of bots in spreading religious hatred in Arabic Twitter space. 
Do they propose a new model to better detect Arabic bots specifically?

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?

