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]: CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data.
[EX A]: How big is new question answering dataset?

[EX Q]: A data collection process involving location neutral keywords used by gang members, with an expanded search of their retweet, friends and follower networks, led to identifying 400 authentic gang member profiles on Twitter. Our study discovered that the text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles.
[EX A]: How is the ground truth of gang membership established in this dataset?

[EX Q]: This model uses a particular RNN cell in order to store just relevant information about the given question. Dynamic Memory stores information of entities present in $T$ . The addition of the $s_t^T q$ term in the gating function is our main contribution.
[EX A]:
How does the model recognize entities and their relation to answers at inference time when answers are not accessible?