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: Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing.
Example Output: What is the architecture of their model?

Example Input: The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc).
Example Output: Where did the real production data come from?

Example Input: More than 2,100 texts were paired with 15 questions each, resulting in a total number of approx. 32,000 annotated questions. For 13% of the questions, the workers did not agree on one of the 4 categories with a 3 out of 5 majority, so we did not include these questions in our dataset.

The distribution of category labels on the remaining 87% is shown in Table TABREF10 . 14,074 (52%) questions could be answered. Out of the answerable questions, 10,160 could be answered from the text directly (text-based) and 3,914 questions required the use of commonsense knowledge (script-based). After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%). This ratio was manually verified based on a random sample of questions.
Example Output:
what dataset statistics are provided?