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:
This model uses a particular RNN cell in order to store just relevant information about the given question.  Dynamic Memory stores information of entities present in $T$ . The addition of the $s_t^T q$ term in the gating function is our main contribution.  
 Question: How does the model recognize entities and their relation to answers at inference time when answers are not accessible?

Ex Output:
gating function Dynamic Memory


Ex Input:
The ratio of correct `translations' (matches) was used as an evaluation measure. 
 Question: What evaluation metric do they use?

Ex Output:
Accuracy


Ex Input:
the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence In particular, we analyzed how the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence. 
 Question: How does their model improve interpretability compared to softmax transformers?

Ex Output:

the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence