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Context Matters for Image Description Evaluation: Challenges for Referenceless Metrics

Elisa Kreiss
Shayan Hooshmand
Eric Zelikman
Meredith Ringel Morris
Christopher Potts
EMNLP 2022 (2022) (to appear)

Abstract

Few images on the Web receive alt-text descriptions that would make them accessible to blind and low vision (BLV) users. Image-based NLG systems have progressed to the point where they can begin to address this persistent societal problem, but these systems will not be fully successful unless we evaluate them on metrics that guide their development correctly. Here, we argue against current referenceless metrics -- those that don't rely on human-generated ground-truth descriptions -- on the grounds that they do not align with the needs of BLV users. The fundamental shortcoming of these metrics is that they cannot take context into account, whereas contextual information is highly valued by BLV users. To substantiate these claims, we present a study with BLV participants who rated descriptions along a variety of dimensions. An in-depth analysis reveals that the lack of context-awareness makes current referenceless metrics inadequate for advancing image accessibility, requiring a rethinking of referenceless evaluation metrics for image-based NLG systems.