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]: This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?
[EX A]: Yes

[EX Q]: As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute). 
 Question: How much better performance of proposed model compared to answer-selection models?
[EX A]: significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)

[EX Q]: terms extracted from Yahoo! Answers tend to be more related, in terms of the number of correlated terms, to attributes related to religion or ethnicity compared to terms from Twitter. However, for two particular attributes (i.e., Price and Buddhist), the number of correlated terms from Twitter is higher than the ones from Yahoo! Answers .  
 Question: On Twitter, do the demographic attributes and answers show more correlations than on Yahoo! Answers?
[EX A]:
No