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.

Input: Consider Input: 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?

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


Input: Consider Input: Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever. 
 Question: Which dialog datasets did they experiment with?

Output: Camrest InCar Assistant


Input: Consider Input:  the TransE model Embedding models The Unstructured model BIBREF22 The TransH model BIBREF26 DISTMULT BIBREF45 ConvE BIBREF51 and ConvKB BIBREF52 The Path Ranking Algorithm (PRA) BIBREF21 
 Question: What models does this overview cover?


Output: This article presented a brief overview of embedding models of entity and relationships for KB completion.