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.

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

We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What existing approaches do they compare to?
Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10

Our model adopts the recently introduced biaffine attention BIBREF14 to enhance our role scorer.  
 Question: What is the biaffine scorer?

biaffine attention BIBREF14