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 evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12. Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset. 
 Question: What dataset is used?
answer:
Twitter dataset obtained from the authors of BIBREF12


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:
In this experiment, we compute the correlation of the proposed NED measure with the patient-perceived emotional bond ratings. Since the proposed measure is asymmetric in nature, we compute the measures for both patient-to-therapist and therapist-to-patient entrainment. We report Pearson's correlation coefficients ( INLINEFORM0 ) for this experiment in Table TABREF26 along with their INLINEFORM1 -values.  
 Question: How do they correlate NED with emotional bond levels?
answer:

They compute Pearson’s correlation between NED measure for patient-to-therapist and patient-perceived emotional bond rating and NED measure for therapist-to-patient and patient-perceived emotional bond rating