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.

We evaluate performance of our models on the SQUAD BIBREF16 dataset (denoted $\mathcal {S}$ ). We use the same split as that of BIBREF4 , where a random subset of 70,484 instances from $\mathcal {S}\ $ are used for training ( ${\mathcal {S}}^{tr}$ ), 10,570 instances for validation ( ${\mathcal {S}}^{val}$ ), and 11,877 instances for testing ( ${\mathcal {S}}^{te}$ ).
Which datasets are used to train this model?

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. 
How many paraphrases are generated per question?

We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. 

Do they report results only on English data?