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]: Experimental Setup 
 Question: what english datasets were used?
[A]: Answer with content missing: (Data section) Penn Treebank (PTB)


[Q]: 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?
[A]: They look at the performance accuracy of explanation and the prediction performance


[Q]:  In particular, the best-performing systems at SemEval-2015 and SemEval-2016 used deep convolutional networks BIBREF53 , BIBREF54  
 Question: What is the current SOTA for sentiment analysis on Twitter at the time of writing?
[A]:
deep convolutional networks BIBREF53 , BIBREF54