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


question:
But we also showed that in some cases the saliency maps seem to not capture the important input features.  The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates
answer:
Is the explanation from saliency map correct?


question:
In order to understand the latent topics of those #MeToo tweets for college followers, we first utilize Latent Dirichlet Allocation (LDA) to label universal topics demonstrated by the users. Since certain words frequently appear in those #MeToo tweets (e.g., sexual harassment, men, women, story, etc.), we transform our corpus using TF-IDF, a term-weighting scheme that discounts the influence of common terms.
answer:

How are the topics embedded in the #MeToo tweets extracted?