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.

The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . 
 Question: what models did they compare to?
High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM 

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 

We evaluated our detection models on three benchmarks: the FCE test data (41K tokens) and the two alternative annotations of the CoNLL 2014 Shared Task dataset (30K tokens) BIBREF3 .  
 Question: Which annotated corpus did they use?

 FCE test data (41K tokens) and the two alternative annotations of the CoNLL 2014 Shared Task dataset (30K tokens)