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]: 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.


[Q]: jiant System Overview ::: jiant Components
Tasks: Tasks have references to task data, methods for processing data, references to classifier heads, and methods for calculating performance metrics, and making predictions. 
 Question: Does jiant involve datasets for the 50 NLU tasks?
[A]: Yes


[Q]: Instead of providing an analytic approximation, AEM uses a discriminator network to discriminate between the reconstructed documents from latent events and the original input documents. This essentially helps the generator to construct a more realistic document from a random noise drawn from a Dirichlet distribution. Due to the flexibility of neural networks, the generator is capable of learning complicated nonlinear distributions. And the supervision signal provided by the discriminator will help generator to capture the event-related patterns. 
 Question: How does this model overcome the assumption that all words in a document are generated from a single event?
[A]:
flexibility of neural networks, the generator is capable of learning complicated nonlinear distributions supervision signal provided by the discriminator will help generator to capture the event-related patterns