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.

Example Input: 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?
Example Output: 10K user-generated image (snap) and textual caption pairs

Example Input: word embedding, input encoder, alignment, aggregation, and prediction 
 Question: what is the architecture of the baseline model?
Example Output: word embedding, input encoder, alignment, aggregation, and prediction.

Example Input: To verify our assumption that target encoding and orthogonal regularization help to boost the diversity of generated sequences, we use two metrics, one quantitative and one qualitative, to measure diversity of generation. First, we simply calculate the average unique predictions produced by both INLINEFORM0 and INLINEFORM1 in experiments shown in Section SECREF36 .  In this example there are 29 ground truth phrases. Neither of the models is able to generate all of the keyphrases, but it is obvious that the predictions from INLINEFORM0 all start with “test”, while predictions from INLINEFORM1 are diverse. 
 Question: How is keyphrase diversity measured?
Example Output:
average unique predictions illustrate the difference of predictions between our proposed models, we show an example chosen from the KP20k validation set

