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: We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion.
Example Output: What are the deep learning architectures used in the task?

Example Input: We introduce a new model for difficulty prediction combining learned representations induced via a pre-trained `universal' sentence encoder BIBREF6 , and a sentence encoder learned from scratch for this task. In total, this resulted in 57,505 and 2,428 sentences in the train and test set abstracts, respectively.
Example Output: How much data is needed to train the task-specific encoder?

Example Input: Therefore, we used the crowdsourcing platform CrowdFlower (CF) for our data collection.
Example Output:
How was this data collected?