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.

Input: Consider Input: We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions.

Output: How do they introduce language variation?


Input: Consider Input: SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary.

Output: How does soft contextual data augmentation work?


Input: Consider Input: When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that outperforms both of these with a considerable gap, obtaining an accuracy of $42\%$ and $55\%$ in detecting errors.


Output: Can this adversarial approach be used to directly improve model accuracy?