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 choice of two methods for our empirical study is motivated by the best performance achieved by Logistic Regression in question-question similarity at SemEval 2017 (best system BIBREF37 and second best system BIBREF38 ), and the high performance achieved by neural networks on larger datasets such as SNLI BIBREF13 , BIBREF39 , BIBREF40 , BIBREF41  
 Question: What machine learning and deep learning methods are used for RQE?
[EX A]: Logistic Regression neural networks

[EX Q]: Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.

Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 . 
 Question: Which word embeddings do they use to represent medical visits?
[EX A]: GloVe concatenation of average embeddings calculated separately for the interview and for the medical examination

[EX Q]: Notably, this increase is observed after the conclusion of the US presidential primaries and during the period of the Democratic and Republican National Conventions and does not reduce even after the conclusion of the US presidential elections held on November 8. 
 Question: What other political events are included in the database?
[EX A]:
US presidential primaries Democratic and Republican National Conventions