Uncovering Patterns in Reviewers’ Feedback to Scene Description Authors


Rosiana Natalie, Jolene Loh, Huei Suen Tan, Joshua Tseng, Hernisa Kacorri, and Kotaro Hara


ASSETS '21: The 23rd International ACM SIGACCESS Conference on Computers and Accessibility, October 2021, Article No.: 93, Pages 1–4, https://doi.org/10.1145/3441852.3476550


Audio descriptions (ADs) can increase access to videos for blind people. Researchers have explored different mechanisms for generating ADs, with some of the most recent studies involving paid novices; to improve the quality of their ADs, novices receive feedback from reviewers. However, reviewer feedback is not instantaneous. To explore the potential for real-time feedback through automation, in this paper, we analyze 1,120 comments that 40 sighted novices received from a sighted or a blind reviewer. We find that feedback patterns tend to fall under four themes: (i) Quality; commenting on different AD quality variables, (ii) Speech Act; the utterance or speech action that the reviewers used, (iii) Required Action; the recommended action that the authors should do to improve the AD, and (iv) Guidance; the additional help that the reviewers gave to help the authors. We discuss which of these patterns could be automated within the review process as design implications for future AD collaborative authoring systems.