TASK DEFINITION: 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.
PROBLEM: Wikilinks can be seen as a large-scale, naturally-occurring, crowd-sourced dataset where thousands of human annotators provide ground truths for mentions of interest. This means that the dataset contains various kinds of noise, especially due to incoherent contexts. We prepare our dataset from the local-context version of Wikilinks, and resolve ground-truth links using a Wikipedia dump from April 2016. We use the page and redirect tables for resolution, and keep the database pageid column as a unique identifier for Wikipedia entities. We discard mentions where the ground-truth could not be resolved (only 3% of mentions).

SOLUTION: How was a quality control performed so that the text is noisy but the annotations are accurate?

PROBLEM:  In the pyramid scoring, the content units in the gold human written summaries are organized in a pyramid. In this pyramid, the content units are organized in tiers and higher tiers of the pyramid indicate higher importance. 

SOLUTION: What manual Pyramid scores are used?

PROBLEM: Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. 

SOLUTION:
How many tweets are in the dataset?