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]: We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task. 
 Question: What are the measures of "performance" used in this paper?
[A]: test-set perplexity, likelihood convergence and clustering measures visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task


[Q]: The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29. 
 Question: Does API provide ability to connect to models written in some other deep learning framework?
[A]: Yes


[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