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.

Example Input: As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score.  
 Question: What were the evaluation metrics used?
Example Output: entity match rate BLEU score Success F1 score

Example Input: To enable this, we use a bifocal attention mechanism which computes an attention over fields at a macro level and over values at a micro level. We then fuse these attention weights such that the attention weight for a field also influences the attention over the values within it. Fused Bifocal Attention Mechanism
Intuitively, when a human writes a description from a table she keeps track of information at two levels. At the macro level, it is important to decide which is the appropriate field to attend to next and at a micro level (i.e., within a field) it is important to know which values to attend to next. To capture this behavior, we use a bifocal attention mechanism as described below. Fused Attention: Intuitively, the attention weights assigned to a field should have an influence on all the values belonging to the particular field. To ensure this, we reweigh the micro level attention weights based on the corresponding macro level attention weights. In other words, we fuse the attention weights at the two levels as: DISPLAYFORM0

where INLINEFORM0 is the field corresponding to the INLINEFORM1 -th value, INLINEFORM2 is the macro level context vector. 
 Question: What is a bifocal attention mechanism?
Example Output: At the macro level, it is important to decide which is the appropriate field to attend to next micro level (i.e., within a field) it is important to know which values to attend to next fuse the attention weights at the two levels

Example Input: As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively 
 Question: How many samples did they generate for the artificial language?
Example Output:
70,000