An error analysis for image-based multi-modal neural machine translation.

We note that mistranslation is arguably the most damaging error type within this category, and that there is a clear trend of MNMT models performing considerably better than both text-only baselines, e.g. sometimes presenting less than half of the number of errors of the baselines.

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