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.

[EX Q]: Although no perfect matches exist, we decided upon the Quora duplicate question dataset BIBREF22 as the best match. To study the embeddings, we computed the euclidean distance between the two questions using various embeddings, to study the distance between semantically similar and dissimilar questions. 
 Question: What existing corpus is used for comparison in these experiments?
[EX A]: Quora duplicate question dataset BIBREF22

[EX Q]: Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: Was the approach used in this work to detect fake news fully supervised?
[EX A]: Yes

[EX 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. 
 Question: Which of their training domains improves performance the most?
[EX A]:
documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

