TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: test-set perplexity, likelihood convergence and clustering measures visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task

PROBLEM: Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. 
 Question: Do they study frequent user responses to help automate modelling of those?

SOLUTION: Yes

PROBLEM: We apply our adaptively sparse Transformers on four machine translation tasks.  IWSLT 2017 German $\rightarrow $ English BIBREF27: 200K sentence pairs.

KFTT Japanese $\rightarrow $ English BIBREF28: 300K sentence pairs.

WMT 2016 Romanian $\rightarrow $ English BIBREF29: 600K sentence pairs.

WMT 2014 English $\rightarrow $ German BIBREF30: 4.5M sentence pairs. 
 Question: What tasks are used for evaluation?

SOLUTION:
four machine translation tasks: German -> English, Japanese -> English, Romanian -> English, English -> German