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In this work we present an approach for generating alternative text (oralt-text) descriptions for images shared on social media, specifically Twitter.More than just a special case of image captioning, alt-text is both moreliterally descriptive and context-specific. Also critically, images posted toTwitter are often accompanied by user-written text that despite not necessarilydescribing the image may provide useful context that if properly leveraged canbe informative. We address this task with a multimodal model that conditions onboth textual information from the associated social media post as well asvisual signal from the image, and demonstrate that the utility of these twoinformation sources stacks. We put forward a new dataset of 371k images pairedwith alt-text and tweets scraped from Twitter and evaluate on it across avariety of automated metrics as well as human evaluation. We show that ourapproach of conditioning on both tweet text and visual informationsignificantly outperforms prior work, by more than 2x on BLEU@4.

Alt-Text with Context: Improving Accessibility for Images on Twitter