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]: SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary.
[EX A]: How does soft contextual data augmentation work?

[EX Q]: For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model.
[EX A]: What previously annotated databases are available?

[EX Q]: We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time
[EX A]:
What types of various community texts have been investigated for exploring global structure of binomials?