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.
--------
Question: As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively 
 Question: How many samples did they generate for the artificial language?

Answer: 70,000


Question: The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement) 
 Question: Based on this paper, what is the more predictive set of features to detect fake news?

Answer: words embeddings, style, and morality features


Question: In cooperation with the ruling regime, Weibo sets strict control over the content published under its service BIBREF0. According to Zhu et al. zhu-etal:2013, Weibo uses a variety of strategies to target censorable posts, ranging from keyword list filtering to individual user monitoring. Among all posts that are eventually censored, nearly 30% of them are censored within 5–30 minutes, and nearly 90% within 24 hours BIBREF1. We hypothesize that the former are done automatically, while the latter are removed by human censors. 
 Question: Is is known whether Sina Weibo posts are censored by humans or some automatic classifier?

Answer:
No