  The field of visual question answering (VQA) has recently seen a surge in
research focused on providing explanations for predicted answers. However,
current systems mostly rely on separate models to predict answers and generate
explanations, leading to less grounded and frequently inconsistent results. To
address this, we propose a multitask learning approach towards a Unified Model
for Answer and Explanation generation (UMAE). Our approach involves the
addition of artificial prompt tokens to training data and fine-tuning a
multimodal encoder-decoder model on a variety of VQA-related tasks. In our
experiments, UMAE models surpass the prior state-of-the-art answer accuracy on
A-OKVQA by 10~15%, show competitive results on OK-VQA, achieve new
state-of-the-art explanation scores on A-OKVQA and VCR, and demonstrate
promising out-of-domain performance on VQA-X.
