TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
PROBLEM: We observe interesting hidden correlation in data. Fig. FIGREF24 has Topic 2 as selected topic. Topic 2 contains top-4 co-occurring keywords "vegan", "yoga", "job", "every_woman" having the highest term frequency. We can infer different things from the topic that "women usually practice yoga more than men", "women teach yoga and take it as a job", "Yogi follow vegan diet". We would say there are noticeable correlation in data i.e. `Yoga-Veganism', `Women-Yoga'. Women-Yoga 
 Question: What other interesting correlations are observed?

SOLUTION: Women-Yoga

PROBLEM: Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. 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. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language.  
 Question: In what way is the offensive dataset not biased by topic, dialect or target?

SOLUTION: It does not use a seed list to gather tweets so the dataset does not skew to specific topics, dialect, targets.

PROBLEM: Both beat the non-attention baseline by a significant margin. For the attention baseline, we use the standard parametrized attention BIBREF2 . 
 Question: Which baseline methods are used?

SOLUTION:
standard parametrized attention and a non-attention baseline