instruction:
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.
question:
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. 
answer:
How many paraphrases are generated per question?


question:
We adapted BERTNLU from ConvLab-2.  We implemented a rule-based model (RuleDST) and adapted TRADE (Transferable Dialogue State Generator) BIBREF19 in this experiment.  We adapted a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy). 
answer:
What are the benchmark models?


question:
Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks. 
answer:

On how many language pairs do they show that preordering assisting language sentences helps translation quality?