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: To evaluate the influence of our hypersphere feature for off-the-shelf NER systems, we perform the NE recognition on two standard NER benchmark datasets, CoNLL2003 and ONTONOTES 5.0.
Example Output: Do they evaluate on NER data sets?

Example Input: Our user study compares the correctness of three scenarios:

Parser correctness - our baseline is the percentage of examples where the top query returned by the semantic parser was correct.

User correctness - the percentage of examples where the user selected a correct query from the top-7 generated by the parser.

Hybrid correctness - correctness of queries returned by a combination of the previous two scenarios. The system returns the query marked by the user as correct; if the user marks all queries as incorrect it will return the parser's top candidate.
Example Output:
Do they conduct a user study where they show an NL interface with and without their explanation?