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.

Example Input: Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability.
Example Output: How was the corpus obtained?

Example Input: Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. As we would expect, on the probe for predicting chunk tags, mSynC achieves 96.9 $F_1$ vs. 92.2 $F_1$ for ELMo-transformer, indicating that mSynC is indeed encoding shallow syntax. Overall, the results further confirm that explicit shallow syntax does not offer any benefits over ELMo-transformer.
Example Output: What are improvements for these two approaches relative to ELMo-only baselines?

Example Input: In this work, we use pretraining on the unlabeled data of each task and show that it can increase the performance of classification systems.
Example Output:
Where is MVCNN pertained?