instruction:
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.
question:
We propose to derive rewards from human-human dialogues by assigning positive values to contextualised responses seen in the data, and negative values to randomly chosen responses due to lacking coherence (also referred to as 'non-human-like responses') see example in Tables TABREF29 and TABREF30 .
Question: How do they obtain human generated policies?
answer:
derive rewards from human-human dialogues by assigning positive values to contextualised responses seen in the data, and negative values to randomly chosen responses due to lacking coherence

question:
Our street view environment was integrated into ParlAI BIBREF6 and used to collect a large-scale dataset on Mechanical Turk involving human perception, action and communication.
Question: What data did they use?
answer:
dataset on Mechanical Turk involving human perception, action and communication

question:
For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength. Specifically, we use the English Wikipedia as the base corpus for such counting.
Question: What is the source of external knowledge?
answer:
counts of predicate-argument tuples from English Wikipedia