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.

Input: Consider Input: 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?

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

Input: Consider Input: Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary.
Question: What different correlations result when using different variants of ROUGE scores?

Output: we observe that many variants of Rouge scores do not have high correlations with human pyramid scores

Input: Consider Input: We have also performed preliminary experiments on the Arabic portion of the SemEval-2016 cQA task.
Question: Did they experimnet in other languages?
Output: Yes