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: A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb. 
 Question: How does the SCAN dataset evaluate compositional generalization?

SOLUTION: it systematically holds out inputs in the training set containing basic primitive verb, "jump", and tests on sequences containing that verb.

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: Since those sets are expected to reflect social norms, they are referred as Dos and Don'ts hereafter. Analogously, some of the negative words just describe inappropriate behaviour, like slur or misdeal, whereas others are real crimes as murder. 
 Question: Do they report results only on English data?

SOLUTION:
Yes