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: Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively.
Example Output: What is the best model?

Example Input: (Information Retrieval). This baseline has been successfully used for related tasks like Question Answering BIBREF39 . We create two versions of this baseline: one with the pool of perspectives INLINEFORM0 and one with the pool of evidences INLINEFORM1 .
Example Output: Which machine baselines are used?

Example Input: S2-GORC is a large citation graph dataset which includes full texts of 8.1 million scientific documents. We select a subset of 154K computer science articles as our corpus. From these, we extract 622K citing sentences that link back to other documents in our corpus. We hold 2500 examples for each of the validation and test sets.
Example Output:
What is the size of the corpus?