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: They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb.
Example Output: How was coverage measured?

Example Input: (2) Different from LEM and DPEMM, AEM uses a generator network to capture the event-related patterns and is able to mine events from different text sources (short and long). Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration;
Example Output: What alternative to Gibbs sampling is used?

Example Input: We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively.
Example Output:
What is the size of the dataset?