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.

Input: Consider 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?

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


Input: Consider Input: First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21. 
 Question: What is the baseline?

Output: CNN modelBIBREF0 Stanford CRF modelBIBREF21


Input: Consider Input: The main reason is that when the controllability is strong, the change of selection will directly affect the text realization so that a tiny error of content selection might lead to unrealistic text. If the selector is not perfectly trained, the fluency will inevitably be influenced. When the controllability is weaker, like in RS, the fluency is more stable because it will not be affected much by the selection mask. 
 Question: Does the performance necessarily drop when more control is desired?


Output: Yes