TASK DEFINITION: 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.
PROBLEM: The policies trained with the NUS achieved an average success rate (SR) of 94.0% and of 96.6% when tested on the ABUS and the NUS, respectively.

SOLUTION: by how much did nus outperform abus?

PROBLEM: 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.

SOLUTION: How many paraphrases are generated per question?

PROBLEM: Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism.

SOLUTION:
What evaluation metrics did the authors look at?