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 tested the natural language representation against the visual-based and feature representations on several tasks, with varying difficulty. The tasks included a basic scenario, a health gathering scenario, a scenario in which the agent must take cover from fireballs, a scenario in which the agent must defend itself from charging enemies, and a super scenario, where a mixture of the above scenarios was designed to challenge the agent. 
 Question: What experiments authors perform?

Output: a basic scenario, a health gathering scenario, a scenario in which the agent must take cover from fireballs, a scenario in which the agent must defend itself from charging enemies, and a super scenario, where a mixture of the above scenarios


Input: Consider Input:  To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.  
 Question: What was their system's performance?

Output: accuracy and F1-score of 89.6% and 89.2%, respectively


Input: Consider Input: For the human evaluation, we follow the standard approach in evaluating machine translation systems BIBREF23 , as used for question generation by BIBREF9 . We asked three workers to rate 300 generated questions between 1 (poor) and 5 (good) on two separate criteria: the fluency of the language used, and the relevance of the question to the context document and answer. 
 Question: What human evaluation metrics were used in the paper?


Output: rating questions on a scale of 1-5 based on fluency of language used and relevance of the question to the context