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.

Ex Input:
Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates.

Ex Output:
Do they use any knowledge base to expand abbreviations?


Ex Input:
Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best.

Ex Output:
What do you use to calculate word/sub-word embeddings


Ex Input:
To demonstrate our method in a black-box setting, we focus our experiments on Google's machine translation system (GMT), accessed through its Cloud API.

Ex Output:

Which neural machine translation system is used?