In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class.
[A]: Is model explanation output evaluated, what metric was used?

[Q]: For the cross-domain polarity classification experiments, we use the second version of Multi-Domain Sentiment Dataset BIBREF0 . The data set contains Amazon product reviews of four different domains: Books (B), DVDs (D), Electronics (E) and Kitchen appliances (K). Reviews contain star ratings (from 1 to 5) which are converted into binary labels as follows: reviews rated with more than 3 stars are labeled as positive, and those with less than 3 stars as negative. In each domain, there are 1000 positive and 1000 negative reviews.
[A]: How long is the dataset?

[Q]: We employ the standard ROUGE-1, ROUGE-2 and ROUGE-L metrics BIBREF29 to evaluate all summarization models. Following BIBREF22, BIBREF23, we use F-measure ROUGE on XSUM and CNN/DailyMail, and use limited-length recall-measure ROUGE on NYT and DUC.
[A]:
What metric was used in the evaluation step?