Multimodal Machine Translation
38 papers with code • 3 benchmarks • 5 datasets
Multimodal machine translation is the task of doing machine translation with multiple data sources - for example, translating "a bird is flying over water" + an image of a bird over water to German text.
( Image credit: Findings of the Third Shared Task on Multimodal Machine Translation )
Benchmarks
These leaderboards are used to track progress in Multimodal Machine Translation
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Libraries
Use these libraries to find Multimodal Machine Translation models and implementations
Multi30K
Hindi Visual Genome
WMT 2016 IT
WMT 2016 Biomedical
Most implemented papers
Attention Is All You Need
tensorflow/tensor2tensor
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Multi30K: Multilingual English-German Image Descriptions
We introduce the Multi30K dataset to stimulate multilingual multimodal research.
On Vision Features in Multimodal Machine Translation
libeineu/fairseq_mmt
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ACL 2022
Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models.
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation
matthieufp/vgamt
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One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images.
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).
Does Multimodality Help Human and Machine for Translation and Image Captioning?
This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge.
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
nmtpy has been used for LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.
A Visual Attention Grounding Neural Model for Multimodal Machine Translation
Eurus-Holmes/VAG-NMT
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EMNLP 2018
The model leverages a visual attention grounding mechanism that links the visual semantics with the corresponding textual semantics.
Findings of the Third Shared Task on Multimodal Machine Translation
In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech.
