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]: We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences.  
 Question: How do they perform data augmentation?
[A]: They randomly sample sentences from Wikipedia that contains an object RC and add them to training data


[Q]: Based on the gained insights, we have improved the state-of-the-art in XNLI for the Translate-Test and Zero-Shot approaches by a substantial margin. 
 Question: Is the improvement over state-of-the-art statistically significant?
[A]: Yes


[Q]: In our dataset, we store the full tweet object including the id of the tweet, username, hashtags, and geolocation of the tweet. We created a list of the most common Arabic keywords associated with COVID-19. Using Twitter’s streaming API, we searched for any tweet containing the keyword(s) in the text of the tweet. Table TABREF1 shows the list of keywords used along with the starting date of tracking each keyword. Furthermore, Table TABREF2 shows the list of hashtags we have been tracking along with the number of tweets collected from each hashtag.  
 Question: What additional information is found in the dataset?
[A]:
the full tweet object including the id of the tweet, username, hashtags, and geolocation of the tweet