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 shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute). 
 Question: How much better performance of proposed model compared to answer-selection models?
Example Output: significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)

Example Input: Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques 
 Question: How is the semi-structured knowledge base created?
Example Output: using a mixture of manual and semi-automatic techniques

Example Input: We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input. The encoder is shared among the sequences simply uses two stacked GRU layers. The interaction part consists of only attention for one model while for the another one it consists of attention and conflict combined as shown in (eqn.11) . The classifier part is simply stacked fully-connected layers.  
 Question: Which neural architecture do they use as a base for their attention conflict mechanisms?
Example Output:
GRU-based encoder, interaction block, and classifier consisting of stacked fully-connected layers.