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 used recordings of nurse-initiated telephone conversations for congestive heart failure patients undergoing telemonitoring, post-discharge from the hospital. To analyze the linguistic structure of the inquiry-response pairs in the entire 41-hour dataset, we randomly sampled a seed dataset consisting of 1,200 turns and manually categorized them to different types, which are summarized in Table TABREF14 along with the corresponding occurrence frequency statistics.
Example Output: Which data do they use as a starting point for the dialogue dataset?

Example Input: In this paper, we use three data sets from the literature to train and evaluate our own classifier. Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as “Harrassing” or “Non-Harrassing”; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader “Harrassing” category BIBREF9 . Many of the false negatives we see are specific references to characters in the TV show “My Kitchen Rules”, rather than something about women in general.  While this may be a limitation of considering only the content of the tweet, it could also be a mislabel.

Debra are now my most hated team on #mkr after least night's ep. Snakes in the grass those two.

Along these lines, we also see correct predictions of innocuous speech, but find data mislabeled as hate speech:

@LoveAndLonging ...how is that example "sexism"?

@amberhasalamb ...in what way?
Example Output: Do they report results only on English data?

Example Input: We have used a dataset of 14 TDs to conduct our experiments.
Example Output:
Which dataset(s) do they use?