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]: The dataset consists of 198,112 news articles. 
 Question: How many articles did they have?
[EX A]: 198,112

[EX Q]: Additionally, we also test their performance only on the items from the Zurich and Visp dialect, because most of the samples are from this two dialects. In Table TABREF15 we show the result of the comparison of the two models. 
 Question: Is the model evaluated on the graphemes-to-phonemes task?
[EX A]:
Yes