  In this paper, we present a novel approach for the task of eXplainable
Question Answering (XQA), i.e., generating natural language (NL) explanations
for the Visual Question Answering (VQA) problem. We generate NL explanations
comprising of the evidence to support the answer to a question asked to an
image using two sources of information: (a) annotations of entities in an image
(e.g., object labels, region descriptions, relation phrases) generated from the
scene graph of the image, and (b) the attention map generated by a VQA model
when answering the question. We show how combining the visual attention map
with the NL representation of relevant scene graph entities, carefully selected
using a language model, can give reasonable textual explanations without the
need of any additional collected data (explanation captions, etc). We run our
algorithms on the Visual Genome (VG) dataset and conduct internal user-studies
to demonstrate the efficacy of our approach over a strong baseline. We have
also released a live web demo showcasing our VQA and textual explanation
generation using scene graphs and visual attention.
