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]: In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST).
Question: What discourse features are used?
[A]: Entity grid with grammatical relations and RST discourse relations.

[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]: We used recordings of nurse-initiated telephone conversations for congestive heart failure patients undergoing telemonitoring, post-discharge from the hospital. To analyze the linguistic structure of the inquiry-response pairs in the entire 41-hour dataset, we randomly sampled a seed dataset consisting of 1,200 turns and manually categorized them to different types, which are summarized in Table TABREF14 along with the corresponding occurrence frequency statistics.
Question: Which data do they use as a starting point for the dialogue dataset?
[A]:
A sample from nurse-initiated telephone conversations for congestive heart failure patients undergoing telepmonitoring, post-discharge from the Health Management Unit at Changi General Hospital