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]: convertible.pl: implementing DCG rules for 1st and 3rd steps in the three-steps conversion, as well as other rules including lexicon.
[EX A]: What DCGs are used?

[EX Q]: Our conversational agent uses two architectures to simulate a specialized reminiscence therapist. The block in charge of generating questions is based on the work Show, Attend and Tell BIBREF13. This work generates descriptions from pictures, also known as image captioning.
[EX A]: Is machine learning system underneath similar to image caption ML systems?

[EX Q]: We deployed the task on Amazon Mechanical Turk (AMT). To see how reasoning varies across workers, we hire 3 crowdworkers per one instance. We hire reliable crowdworkers with $\ge 5,000$ HITs experiences and an approval rate of $\ge $ 99.0%, and pay ¢20 as a reward per instance.
[EX A]:
Did they use any crowdsourcing platform?