Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation

Subaru Kimura, Jun Suzuki


Abstract
In manga translation, visual context outside speech-bubble text, including speaker identities, referents, and scene tone, is depicted in page images, making it natural to refer to images during translation evaluation. However, most conventional manga translation evaluations rely solely on text-only automatic metrics, leaving the impact of image input insufficiently explored. We propose a practical validation framework that employs the same vision-language model (VLM) as both a text-only judge (TOJ) and an image-aware judge (IAJ) to investigate evaluation divergences associated with image-aware evaluation procedures. We use a Japanese-English manga dataset of official translations aligned at the page and speech-bubble levels. First, adding page images to TOJ enabled the primary VLM to detect more degraded translations. Second, we analyzed evaluation divergences between TOJ and IAJ, finding different error counts and error-category distributions. Human verification did not reveal a consistent preference between TOJ and IAJ.
Anthology ID:
2026.wmt-main.20
Volume:
Proceedings of the Eleventh Conference on Machine Translation
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venues:
WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
345–367
Language:
URL:
https://aclanthology.org/2026.wmt-main.20/
DOI:
Bibkey:
Cite (ACL):
Subaru Kimura and Jun Suzuki. 2026. Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation. In Proceedings of the Eleventh Conference on Machine Translation, pages 345–367, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation (Kimura & Suzuki, WMT 2026)
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PDF:
https://aclanthology.org/2026.wmt-main.20.pdf