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.

Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. 
 Question: Which matrix factorization methods do they use?
weighted factorization of a word-context co-occurrence matrix 

To collect article-level labels, we utilized a platform in the company that has been used by the market research team to collect surveys from the subscribers of different news publishers. The survey works as follows: The user is first presented with a set of selected pages (usually 4 pages and around 20 articles) from the print paper the day before. The user can select an article each time that he or she has read, and answer some questions about it. We added 3 questions to the existing survey that asked the level of partisanship, the polarity of partisanship, and which pro- or anti- entities the article presents. We also asked the political standpoint of the user. The complete survey can be found in Appendices. 
 Question: Did they crowdsource the annotations?
Yes

We also include RNNLM BIBREF11 , a recurrent neural network based language model in the comparison. 
 Question: Which language models do they compare against?

RNNLM BIBREF11