  GQA (Hudson and Manning, 2019) is a dataset for real-world visual reasoning
and compositional question answering. We found that many answers predicted by
the best visionlanguage models on the GQA dataset do not match the ground-truth
answer but still are semantically meaningful and correct in the given context.
In fact, this is the case with most existing visual question answering (VQA)
datasets where they assume only one ground-truth answer for each question. We
propose Alternative Answer Sets (AAS) of ground-truth answers to address this
limitation, which is created automatically using off-the-shelf NLP tools. We
introduce a semantic metric based on AAS and modify top VQA solvers to support
multiple plausible answers for a question. We implement this approach on the
GQA dataset and show the performance improvements.
