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.

In a second step, we will train a neural monolingual translation system, that translates from the output of the PBMT system INLINEFORM0 to a better target sentence INLINEFORM1 .
Question: Do they train the NMT model on PBMT outputs?
Yes

The SnapCaptions dataset is composed of 10K user-generated image (snap) and textual caption pairs where named entities in captions are manually labeled by expert human annotators (entity types: PER, LOC, ORG, MISC).
Question: How large is their MNER SnapCaptions dataset?
10K user-generated image (snap) and textual caption pairs

Once the S-V-O is generated, Text2Visual provides users with visual components that convey the S-V-O text meanings.
Question: Does their solution involve connecting images and text?
Yes