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.

Q: 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?

A: gating function Dynamic Memory
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Q: One example illustrates that the mistakes the model makes can be associated with changes in the party policy.
Question: Do changes in policies of the political actors account for all of the mistakes the model made?

A: Yes
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Q: The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question.
Question: What is an "answer style"?

A:
well-formed sentences vs concise answers
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