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.

  In a second step, we will train a neural monolingual translation system, that translates from the output of the PBMT system INLINEFORM0 to a better target sentence INLINEFORM1 . 
 Question: Do they train the NMT model on PBMT outputs?
Yes

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

The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. 
 Question: Which deep learning model performed better?

autoencoders