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 Input:
In the experiments, we consider four data sets, two of them newly created and the remaining two already public: CNN, TIME, 20 Newsgroups, and Reuters-21578. The code and the two new data sets are available at github.com/baiyangwang/emgan. For the pre-processing of all the documents, we transformed all characters to lower case, stemmed the documents, and ran the word2vec model on each corpora to obtain word embeddings with a size of 300. In all subsequent models, we only consider the most frequent INLINEFORM0 words across all corpora in a data set. 
 Question: Which corpora do they use?

Ex Output:
CNN, TIME, 20 Newsgroups, and Reuters-21578


Ex Input:
We consider QA PGNet and Multi-decoder QA PGNet with lookup table embedding as baseline models and improve on the baselines with other variations described below. 
 Question: What is the baseline?

Ex Output:
QA PGNet Multi-decoder QA PGNet with lookup table embedding


Ex Input:
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 Output:

Continuous Bag-of-Words (CBOW)