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.

Input: Consider Input: 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?

Output: 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

Input: Consider Input: Experimental Setup
Question: what english datasets were used?

Output: Answer with content missing: (Data section) Penn Treebank (PTB)

Input: Consider Input: We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.
Question: What knowledge bases do they use?
Output: NELL