TASK DEFINITION: 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.
PROBLEM: The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs. 
 Question: How well did the baseline perform?

SOLUTION: accuracy of 30.3% on single sentences and 0.3 on complete paragraphs

PROBLEM: Most lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
 Question: How was the training data translated?

SOLUTION: using the machine translation platform Apertium 

PROBLEM:  For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English .  
 Question: What monolingual word representations are used?

SOLUTION:
AraVec for Arabic, FastText for French, and Word2vec Google News for English.