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.

Q: The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25).
Question: Which five natural disasters were examined?

A: the East Coast Bomb Cyclone the Mendocino, California wildfires Hurricane Florence Hurricane Michael the California Camp Fires
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Q: Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks.
Question: What hyperparameters have been tuned?

A: number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks
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Q: 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?

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