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.

[EX Q]: 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?
[EX A]: words embeddings, style, and morality features

[EX Q]: The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word.  
 Question: Which word embeddings are analysed?
[EX A]: Continuous Bag-of-Words (CBOW)

[EX Q]: We extracted Japanese word pairs from the Evaluation Dataset of Japanese Lexical Simplification kodaira.  
 Question: where does the data come from?
[EX A]:
Evaluation Dataset of Japanese Lexical Simplification kodaira

