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: Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. 
 Question: Which matrix factorization methods do they use?
Example Output: weighted factorization of a word-context co-occurrence matrix 

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: We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective. 
 Question: What objective function is used in the GAN?
Example Output:
language modeling objective