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Detecting Concrete Visual Tokens for Multimodal Machine Translation
The challenge of visual grounding and masking in multimodal machine translation (MMT) systems has encouraged varying approaches to the detection and selection of visually-grounded text tokens for masking.
Adding Multimodal Capabilities to a Text-only Translation Model
While most current work in multimodal machine translation (MMT) uses the Multi30k dataset for training and evaluation, we find that the resulting models overfit to the Multi30k dataset to an extreme degree.
The Case for Evaluating Multimodal Translation Models on Text Datasets
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.
Pairwise versus multiple network alignment
Just as the recent trend in the NA field, we also focus on global NA, which can be pairwise (PNA) and multiple (MNA).
