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]: It is worth mentioning that the collected texts contain a large quantity of errors of several types: orthographic, syntactic, code-switched words (i.e. words not in the required language), jokes, etc. Hence, the original written sentences have been processed in order to produce “cleaner” versions, in order to make the data usable for some research purposes (e.g. to train language models, to extract features for proficiency assessment, ...).
[EX A]: Are any of the utterances ungrammatical?

[EX Q]: In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am.
[EX A]: what is the source of the news sentences?

[EX Q]: In this paper, we study verifiable robustness, i.e., providing a certificate that for a given network and test input, no attack or perturbation under the specification can change predictions, using the example of text classification tasks, Stanford Sentiment Treebank (SST) BIBREF15 and AG News BIBREF16. 
[EX A]:
Which dataset do they use?