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.

[EX Q]:  In almost all genres, DenseNMT models are significantly better than the baselines.
[EX A]: did they outperform previous methods?

[EX Q]: In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.
[EX A]: Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?

[EX Q]:  Here a small portion of the large parallel corpus for English-German is used as a simulation for the scenario where we do not have much parallel data: Translating texts in English to German. 
[EX A]:
Which languages do they test on for the under-resourced scenario?