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]: Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. 
 Question: What evaluation metrics did the authors look at?
[A]: localization accuracy


[Q]: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. 
 Question: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?
[A]: our Open model achieves more than 3 points of f1-score than the state-of-the-art result


[Q]: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. 
 Question: How do they measure performance?
[A]:
average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values