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]: To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class.
[EX A]: How is "hirability" defined?

[EX Q]: In contrast to existing bottlenecks, this work targets three different types of social networks (Formspring: a Q&A forum, Twitter: microblogging, and Wikipedia: collaborative knowledge repository) for three topics of cyberbullying (personal attack, racism, and sexism) without doing any explicit feature engineering by developing deep learning based models along with transfer learning.
[EX A]: What cyberbulling topics did they address?

[EX Q]: The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor.
[EX A]:
Do they single out a validation set from the fixed SRE training set?