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]: Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively. 
 Question: Do they measure computation time of their factorizations compared to other word embeddings?
[EX A]: Yes

[EX Q]: The corpus is freely available at the following link 
 Question: Is this dataset publicly available?
[EX A]: Yes

[EX Q]: We used the public tool, word2vec, released by Mikolov-2013 to obtain the word embeddings. Their neural network approach is similar to the feed-forward neural networks BIBREF5 , BIBREF6 . In the Skip-gram model architecture we used, we have chosen 200 as the dimension of the obtained word vectors. 
 Question: What type and size of word embeddings were used?
[EX A]:
word2vec 200 as the dimension of the obtained word vectors