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.

Example Input: The dataset consists of 198,112 news articles. 
 Question: How many articles did they have?
Example Output: 198,112

Example Input: We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network. 
 Question: How do they measure correlation between the prediction and explanation quality?
Example Output: They look at the performance accuracy of explanation and the prediction performance

Example Input: To find a large amount of hate speech on the refugee crisis, we used 10 hashtags that can be used in an insulting or offensive way. 
 Question: How were potentially hateful messages identified?
Example Output:
10 hashtags that can be used in an insulting or offensive way