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]: Cloze Track User Query Track
Question: What two types the Chinese reading comprehension dataset consists of?
[A]: cloze-style reading comprehension and user query reading comprehension questions

[Q]: Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively.
Question: What is the best model?
[A]:
BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS