@InProceedings{mahendru-EtAl:2017:EMNLP2017,
  author    = {Mahendru, Aroma  and  Prabhu, Viraj  and  Mohapatra, Akrit  and  Batra, Dhruv  and  Lee, Stefan},
  title     = {The Promise of Premise: Harnessing Question Premises in Visual Question Answering},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {926--935},
  abstract  = {In this paper, we make a simple observation that questions about images often
	contain premises -- objects and relationships implied by the question -- and
	that reasoning about premises can help Visual Question Answering (VQA) models
	respond more intelligently to irrelevant or previously unseen questions.
	When presented with a question that is irrelevant to an image, state-of-the-art
	VQA models will still answer purely based on learned language biases, resulting
	in non-sensical or even misleading answers. We note that a visual question is
	irrelevant to an image if at least one of its premises is false (i.e. not
	depicted in the image). We leverage this observation to construct a dataset for
	Question Relevance Prediction and Explanation (QRPE) by searching for false
	premises. We train novel question relevance detection models and show that
	models that reason about premises consistently outperform models that do not.
	We also find that forcing standard VQA models to reason about premises during
	training can lead to improvements on tasks requiring compositional reasoning.},
  url       = {https://www.aclweb.org/anthology/D17-1097}
}