In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?
[A]: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same


[Q]: We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark. 
 Question: How large was the dataset of Danish comments?
[A]: 3600 user-generated comments


[Q]: VQA research began in earnest in late 2014 when the DAQUAR dataset was released BIBREF0 
 Question: From when are many VQA datasets collected?
[A]:
late 2014