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:
 Each passage was labelled by 3 unique annotators. 
 Question: How many annotators were there?

Ex Output:
3 


Ex Input:
We collect $37,263$ concept-sets as the inputs, each of which contains three to five common concepts. These concept-sets are sampled from several large corpora of image/video captions, such that the concepts inside them are more likely to co-occur in natural scenes. The expected concept-sets in our task are supposed to be likely co-occur in natural, daily-life scenes . The concepts in images/videos captions, which usually describe scenes in our daily life, thus possess the desired property. We therefore collect a large amount of caption sentences from a variety of datasets, including VATEX BIBREF4, LSMDC BIBREF12, ActivityNet BIBREF13, and SNLI BIBREF15, forming 1,040,330 sentences in total. 
 Question: Where do the concept sets come from?

Ex Output:
These concept-sets are sampled from several large corpora of image/video captions


Ex Input:
We map each relation type $R(x,y)$ to at least one parametrized natural-language question $q_x$ whose answer is $y$ . 
 Question: How is the input triple translated to a slot-filling task?

Ex Output:

The relation R(x,y) is mapped onto a question q whose answer is y