TASK DEFINITION: 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.
PROBLEM: To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class.

SOLUTION: Is model explanation output evaluated, what metric was used?

PROBLEM: We aim to find such content in the social media focusing on the tweets.

SOLUTION: Does the dataset contain content from various social media platforms?

PROBLEM: Generally, adversarial training improves the accuracy across all languages, and the improvement is sometimes dramatic versus the BERT non-adversarial baseline. When combined with adversarial learning, the BERT cross-lingual F1 scores increased for German over the non-adversarial baseline, and the scores remained largely the same for Spanish and Dutch.

SOLUTION:
Does adversarial learning have stronger performance gains for text classification, or for NER?