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?
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