Toward Supporting Quality Alt Text in Computing Publications
AuthorsCandace Williams, Lilian de Greef, Ed Harris, Leah Findlater, Amy Pavel, Cynthia Bennett
AuthorsCandace Williams, Lilian de Greef, Ed Harris, Leah Findlater, Amy Pavel, Cynthia Bennett
While researchers have examined alternative (alt) text for social media and news contexts, few have studied the status and challenges for authoring alt text of figures in computing-related publications. These figures are distinct, often conveying dense visual information, and may necessitate unique accessibility solutions. Accordingly, we explored how to support authors in creating alt text in computing publications---specifically in the field of human-computer interaction (HCI). We conducted two studies: (1) an analysis of 300 recently published figures at a general HCI conference (ACM CHI), and (2) interviews with 10 researchers in HCI and related fields who have varying levels of experience writing alt text. Our findings characterize the prevalence, quality, and patterns of recent figure alt text and captions. We further identify challenges authors encounter, describing their workflow barriers and confusions around how to compose alt text for complex figures. We conclude by outlining a research agenda on process, education, and tooling opportunities to improve alt text in computing-related publications.
In the fast-evolving world of natural language processing (NLP), there is a strong demand for generating coherent and controlled text, as referenced in the work Toward Controlled Generation of Text. Traditional autoregressive models such as GPT, which have long been the industry standard, possess inherent limitations that sometimes manifest as repetitive and low-quality outputs, as seen in the work The Curious Case of Neural Text Degeneration. This is primarily due to a phenomenon known as "exposure bias," as seen in the work Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. This imperfection arises due to a mismatch between how these models are trained and their actual use during inference, often leading to error accumulation during text generation.