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.

[Q]: To tackle this issue we applying a back-translation method BIBREF13, where we translate indirect and physical harassment tweets of the train set from english to german, french and greek. After that, we translate them back to english in order to achieve data augmentation.
[A]: What language(s) is/are represented in the dataset?

[Q]: In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.
[A]: Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?

[Q]: We created this CORD-19-NER dataset with comprehensive named entity annotation on the CORD-19 corpus (2020-03-13). The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13).
[A]:
What is the size of this dataset?