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.
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Question: Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference. 
 Question: How many layers are there in their model?

Answer: two LSTM layers


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: The type and correctness of all the question answer pairs are verified by at least two annotators. 
 Question: What was the inter-annotator agreement?

Answer:
correctness of all the question answer pairs are verified by at least two annotators