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: Finally, we combine our CNN and RNN models using a voting process. For each sentence in the test set, we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes. In case of a tie, we pick one of the most frequent classes randomly.
Example Output: How does their simple voting scheme work?

Example Input: We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
Example Output: What evaluation metrics did they use?

Example Input: For comparison, we also report the performance of the error detection system by Rei2016, trained using the same FCE dataset.
Example Output:
What was the baseline used?

