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:
For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. 

Ex Output:
How many paraphrases are generated per question?


Ex Input:
Bag-of-words feature vectors were used to train a multinomial logistic regression model. Let INLINEFORM0 be the true label, where INLINEFORM1 is the total number of labels and INLINEFORM2 is the concatenation of the weight vectors INLINEFORM3 associated with the INLINEFORM4 th party then DISPLAYFORM0

Ex Output:
What model are the text features used in to provide predictions?


Ex Input:
This paper explores the potential of predicting a user's industry –the aggregate of enterprises in a particular field– by identifying industry indicative text in social media. 

Ex Output:

What do they mean by a person's industry?