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.
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Question: 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?

Answer: gating function Dynamic Memory


Question: We conducted the human evaluation using Amazon Mechanical Turk to assess subjective quality. We recruit master level workers (who have good prior approval rates) to perform a human comparison between generated responses from two systems (which are randomly sampled from comparison systems). The workers are required to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness. Informativeness indicates the extent to which generated utterance contains all the information specified in the dialog act. Naturalness denotes whether the utterance is as natural as a human does. To reduce judgement bias, we distribute each question to three different workers. Finally, we collected in total of 5800 judges. 
 Question: What was the criteria for human evaluation?

Answer: to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness


Question: To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 
 Question: what are the topics pulled from Reddit?

Answer:
politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage.