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.

[Q]: The functional dissimilarity score was computed using sentences from the test set in CoNLL 2017 Universal Dependencies task BIBREF20 for the relevant languages with the provided UPOS sequences. We computed the nearest neighbours experiment for all languages in the training data for the above models. 
 Question: Which evaluation metrics do they use for language modelling?
[A]:  functional dissimilarity score nearest neighbours experiment


[Q]: As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute). 
 Question: How much better performance of proposed model compared to answer-selection models?
[A]: significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)


[Q]: Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representations methods. It can be seen that the NLP representation outperforms the other methods.  NLP representations remain robust to changes in the environment as well as task-nuisances in the state.  
 Question: What result from experiments suggest that natural language based agents are more robust?
[A]:
Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances