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.

Ex Input:
The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression.
Question: What insights into the relationship between demographics and mental health are provided?

Ex Output:
either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age more women than men were given a diagnosis of depression

Ex Input:
As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus, making the problem "more well-defined" by reducing the problem to supervised learning.
Question: Why is this work different from text-only UNMT?

Ex Output:
the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus

Ex Input:
1) Typical Methods. Three typical knowledge graph embedding methods includes TransE, TransR and TransH are selected as baselines. 2) Path-based Methods. We compare our method with two typical path-based model include PTransE, and ALL-PATHS BIBREF18. 3) Attribute-incorporated Methods. Several state-of-art attribute-incorporated methods including R-GCN BIBREF24 and KR-EAR BIBREF26 are used to compare with our methods on three real datasets.
Question: What seven state-of-the-art methods are used for comparison?

Ex Output:
TransE, TransR and TransH PTransE, and ALL-PATHS R-GCN BIBREF24 and KR-EAR BIBREF26