The Case for Evaluating Multimodal Translation Models on Text Datasets
Vipin Vijayan Braeden Bowen Scott Grigsby Timothy Anderson Jeremy Gwinnup
A good evaluation framework should evaluate multimodal machine translation
(MMT) models by measuring 1) their use of visual information to aid in the
translation task and 2) their ability to translate complex sentences such as
done for text-only machine translation. However, most current work in MMT is
evaluated against the Multi30k testing sets, which do not measure these
properties. Namely, the use of visual information by the MMT model cannot be
shown directly from the Multi30k test set results and the sentences in Multi30k
are are image captions, i.e., short, descriptive sentences, as opposed to
complex sentences that typical text-only machine translation models are
evaluated against.
  Therefore, we propose that MMT models be evaluated using 1) the CoMMuTE
evaluation framework, which measures the use of visual information by MMT
models, 2) the text-only WMT news translation task test sets, which evaluates
translation performance against complex sentences, and 3) the Multi30k test
sets, for measuring MMT model performance against a real MMT dataset. Finally,
we evaluate recent MMT models trained solely against the Multi30k dataset
against our proposed evaluation framework and demonstrate the dramatic drop
performance against text-only testing sets compared to recent text-only MT
models.