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.

Example Input: In this section, we describe our data collection for these 3 components. We first describe a series of pilot studies that we conducted in order to collect commonsense inference questions (Section SECREF4 ). In Section SECREF5 , we discuss the resulting data collection of questions, texts and answers via crowdsourcing on Amazon Mechanical Turk (henceforth MTurk). Section SECREF17 gives information about some necessary postprocessing steps and the dataset validation. 
Example Output: how was the data collected?

Example Input: Task: given a caption and a paired image (if used), the goal is to label every token in a caption in BIO scheme (B: beginning, I: inside, O: outside) BIBREF27 . 
Example Output: Can named entities in SnapCaptions be discontigious?

Example Input: The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism.
Example Output:
Where do they employ feature-wise sigmoid gating?