TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description.

SOLUTION: How do you find the entity descriptions?

PROBLEM: As shown in Table TABREF46, consensus dropout fusion improves the score of NDCG by around 1.0 from the score of the joint model while still yielding comparable scores for other metrics. As also shown in Table TABREF46, the ensemble model seems to take the best results from each model.

SOLUTION: Which method for integration peforms better ensemble or consensus dropout fusion with shared parameters?

PROBLEM: We consider three benchmark datasets: ( INLINEFORM0 ) Cora, a paper citation network with text information, built by BIBREF44 . Hepth, a paper citation network from Arxiv on high energy physics theory, with paper abstracts as text information. ( INLINEFORM2 ) Zhihu, a Q&A network dataset constructed by BIBREF9 , which has 10,000 active users with text descriptions and their collaboration links.

SOLUTION:
Which dataset of texts do they use?