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.

[EX Q]: We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
[EX A]: What is the source of the user interaction data? 

[EX 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.
[EX A]: How do you find the entity descriptions?

[EX Q]: In this work, we use MLP (modified to handle our data representation) as the base classifier.
[EX A]:
Which classification algorithm do they use for s2sL?