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.

Ex Input:
CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data.

Ex Output:
How big is new question answering dataset?


Ex Input:
We found that 51.25% of the gang members collected have a tweet that links to a YouTube video. Following these links, a simple keyword search for the terms gangsta and hip-hop in the YouTube video description found that 76.58% of the shared links are related to hip-hop music, gangster rap, and the culture that surrounds this music genre.

Ex Output:
What are the differences in the use of YouTube links between gang member and the rest of the Twitter population?


Ex Input:
We identified some limitations during the process, which we describe in this section.

When deciding publisher partisanship, the number of people from whom we computed the score was small. For example, de Stentor is estimated to reach 275K readers each day on its official website. Deciding the audience leaning from 55 samples was subject to sampling bias. Besides, the scores differ very little between publishers. None of the publishers had an absolute score higher than 1, meaning that even the most partisan publisher was only slightly partisan. Deciding which publishers we consider as partisan and which not is thus not very reliable.

The article-level annotation task was not as well-defined as on a crowdsourcing platform. We included the questions as part of an existing survey and didn't want to create much burden to the annotators. Therefore, we did not provide long descriptive text that explained how a person should annotate an article. We thus run under the risk of annotator bias. This is one of the reasons for a low inter-rater agreement.

Ex Output:

What limitations are mentioned?