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Found 5 papers, 2 papers with code
Towards Zero-Shot Multimodal Machine Translation
Current multimodal machine translation (MMT) systems rely on fully supervised data (i. e models are trained on sentences with their translations and accompanying images).
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus
We additionally train two types of multilingual model to prove the benefits of mOSCAR: (1) a model trained on a subset of mOSCAR and captioning data and (2) a model train on captioning data only.
MAD Speech: Measures of Acoustic Diversity of Speech
Using these datasets, we demonstrate that our proposed metrics achieve a stronger agreement with the ground-truth diversity than baselines.
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation
One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images.
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect known for its frequent usage of code-switching.
