Lost in Translation? Translation Errors and Challenges for Fair
  Assessment of Text-to-Image Models on Multilingual Concepts
Michael Saxon Yiran Luo Sharon Levy Chitta Baral Yezhou Yang William Yang Wang
Benchmarks of the multilingual capabilities of text-to-image (T2I) models
compare generated images prompted in a test language to an expected image
distribution over a concept set. One such benchmark, "Conceptual Coverage
Across Languages" (CoCo-CroLa), assesses the tangible noun inventory of T2I
models by prompting them to generate pictures from a concept list translated to
seven languages and comparing the output image populations. Unfortunately, we
find that this benchmark contains translation errors of varying severity in
Spanish, Japanese, and Chinese. We provide corrections for these errors and
analyze how impactful they are on the utility and validity of CoCo-CroLa as a
benchmark. We reassess multiple baseline T2I models with the revisions, compare
the outputs elicited under the new translations to those conditioned on the
old, and show that a correction's impactfulness on the image-domain benchmark
results can be predicted in the text domain with similarity scores. Our
findings will guide the future development of T2I multilinguality metrics by
providing analytical tools for practical translation decisions.