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]: Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts.  
 Question: How much more coverage is in the new dataset?
[A]: 278 more annotations


[Q]: The second Turkish dataset is the Twitter corpus which is formed of tweets about Turkish mobile network operators. Those tweets are mostly much noisier and shorter compared to the reviews in the movie corpus. In total, there are 1,716 tweets. 973 of them are negative and 743 of them are positive. These tweets are manually annotated by two humans, where the labels are either positive or negative. 
 Question: What details are given about the Twitter dataset?
[A]: Those tweets are mostly much noisier and shorter compared to the reviews in the movie corpus. In total, there are 1,716 tweets. 973 of them are negative and 743 of them are positive.


[Q]: We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 . 
 Question: How is the data annotated?
[A]:
The data are self-reported by Twitter users and then verified by two human experts.