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: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress.
Example Output: What are the corpora used for the task?

Example Input: Interesting prior work on quantifying social norm violation has taken a heavily data-driven focus BIBREF8 , BIBREF9 . 
Example Output: Does this paper propose a new task that others can try to improve performance on?

Example Input: We need good amount of data to try deep learning state-of-the-art algorithms. There are lot of open datasets available for names, locations, organisations, but not for topics as defined in Abstract above. Also defining and inferring topics is an individual preference and there are no fix set of rules for its definition. But according to our definition, we can use wikipedia titles as our target topics. English wikipedia dataset has more than 18 million titles if we consider all versions of them till now. We had to clean up the titles to remove junk titles as wikipedia title almost contains all the words we use daily.  After doing some more cleaning we were left with 10 million titles. We have a dump of 15 million English news articles published in past 4 years. Further, we reduced number of articles by removing duplicate and near similar articles. We used our pre-trained doc2vec models and cosine similarity to detect almost similar news articles.
Example Output:
How large is the dataset they used?