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 Q]: We map each relation type $R(x,y)$ to at least one parametrized natural-language question $q_x$ whose answer is $y$ .
[EX A]: How is the input triple translated to a slot-filling task?

[EX Q]: We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.
[EX A]: Which vision-based approaches does this approach outperform?

[EX Q]: First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.
[EX A]:
Why is supporting fact supervision necessary for DMN?