In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Example Input: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.
Example Output: How big is dataset used for fine-tuning BERT?

Example Input: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. The Mixture module brings the image and caption embeddings to a joint feature embedding space.
Example Output: How do the authors define a differential network?

Example Input: The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. 
Example Output:
Which word embeddings are analysed?