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:
After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. 
 Question: How many sentence transformations on average are available per unique sentence in dataset?

Ex Output:
27.41 transformation on average of single seed sentence is available in dataset.


Ex Input:
The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score. 
 Question: What evaluation metrics were used for the summarization task?

Ex Output:
ROUGE BIBREF22 unigram score


Ex Input:
We address this tension by training vector space models to represent the data, in which each unique word in a large corpus is represented by a vector (embedding) in high-dimensional space. The geometry of the resulting vector space captures many semantic relations between words.  We address this tension by training vector space models to represent the data, in which each unique word in a large corpus is represented by a vector (embedding) in high-dimensional space. The geometry of the resulting vector space captures many semantic relations between words.  
 Question: Do they model semantics 

Ex Output:

Yes