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: In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.

A: Which of their training domains improves performance the most?
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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, ...).

A: Are any of the utterances ungrammatical?
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Q: As introduced above, the 'One Million Post' corpus provides annotation labels for more than 11,000 user comments. Although there is no directly comparable category capturing 'offensive language' as defined in the shared task, there are two closely related categories. From the resource, we extract all those comments in which a majority of the annotators agree that they contain either 'inappropriate' or 'discriminating' content, or none of the aforementioned. We treat the first two cases as examples of 'offense' and the latter case as examples of 'other'.

A:
What are the near-offensive language categories?
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