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]: Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below. 
 Question: Why did they choose WER as evaluation metric?
[EX A]: WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses

[EX Q]: Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence. 
 Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?
[EX A]: NCEL considers only adjacent mentions.

[EX Q]: These datasets are part of SemEval-2016 Challenge Task 5 BIBREF27 , BIBREF28 . Table TABREF7 shows the number of observations in each test corpus. 
 Question: what datasets were used in evaluation?
[EX A]:
SemEval-2016 Challenge Task 5 BIBREF27 , BIBREF28