In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: We opted for an automatic approach instead, that can be scaled arbitrarily and at little cost: we generate synthetic sentence pairs $(, ilde{})$ by randomly perturbing 1.8 million segments $$ from Wikipedia. We use three techniques: mask-filling with BERT, backtranslation, and randomly dropping out words.
Question: How are the synthetic examples generated?
[EX A]: Random perturbation of Wikipedia sentences using mask-filling with BERT, backtranslation and randomly drop out

[EX Q]: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .
Question: Where are the hotel reviews from?
[EX A]: TripAdvisor

[EX Q]: In this work, we introduce a methodology that provides VQA algorithms with the ability to generate human interpretable attention maps which effectively ground the answer to the relevant image regions. We accomplish this by leveraging region descriptions and object annotations available in the Visual Genome dataset, and using these to automatically construct attention maps that can be used for attention supervision, instead of requiring human annotators to manually provide grounding labels.
Question: How do they obtain region descriptions and object annotations?
[EX A]:
they are available in the Visual Genome dataset