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: In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning.
Example Output: How does their model differ from BERT?

Example Input: To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning “diabetes", “cancer", and “influenza" into a topic that has an overall “disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
Example Output: How were topics of interest about DDEO identified?

Example Input: Our systems are attentional encoder-decoder networks BIBREF0 . We base our implementation on the dl4mt-tutorial, which we enhanced with new features such as ensemble decoding and pervasive dropout.
Example Output:
what are the baseline systems?