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.

To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.
How do they ensure the generated questions are unanswerable?

We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely. 
What datasets are used for experiments?

We chose Conditional Copy (CC) model as our baseline, which is the best model in Wiseman. 

What is the strong baseline?