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]: 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?
[A]: 10K user-generated image (snap) and textual caption pairs


[Q]: The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model.  
 Question: What do the models that they compare predict?
[A]: national dialects of English


[Q]: We observe $R^2$ correlation coefficients between 0.11 and 0.22 for the input and output length ratios and between 0.81 and 0.88 for the compound divergence.  
 Question: How strong is negative correlation between compound divergence and accuracy in performed experiment?
[A]:
 between 0.81 and 0.88