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        <title>Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation</title>
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    <abstract>Multimodal machine translation (MMT) systems have been successfully developed in recent years for a few language pairs. However, training such models usually requires tuples of a source language text, target language text, and images. Obtaining these data involves expensive human annotations, making it difficult to develop models for unseen text-only language pairs. In this work, we propose the task of zero-shot cross-modal machine translation aiming to transfer multimodal knowledge from an existing multimodal parallel corpus into a new translation direction. We also introduce a novel MMT model with a visual prediction network to learn visual features grounded on multimodal parallel data and provide pseudo-features for text-only language pairs. With this training paradigm, our MMT model outperforms its text-only counterpart. In our extensive analyses, we show that (i) the selection of visual features is important, and (ii) training on image-aware translations and being grounded on a similar language pair are mandatory.</abstract>
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%0 Conference Proceedings
%T Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation
%A Hirasawa, Tosho
%A Bugliarello, Emanuele
%A Elliott, Desmond
%A Komachi, Mamoru
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hirasawa-etal-2023-visual
%X Multimodal machine translation (MMT) systems have been successfully developed in recent years for a few language pairs. However, training such models usually requires tuples of a source language text, target language text, and images. Obtaining these data involves expensive human annotations, making it difficult to develop models for unseen text-only language pairs. In this work, we propose the task of zero-shot cross-modal machine translation aiming to transfer multimodal knowledge from an existing multimodal parallel corpus into a new translation direction. We also introduce a novel MMT model with a visual prediction network to learn visual features grounded on multimodal parallel data and provide pseudo-features for text-only language pairs. With this training paradigm, our MMT model outperforms its text-only counterpart. In our extensive analyses, we show that (i) the selection of visual features is important, and (ii) training on image-aware translations and being grounded on a similar language pair are mandatory.
%R 10.18653/v1/2023.wmt-1.47
%U https://aclanthology.org/2023.wmt-1.47
%U https://doi.org/10.18653/v1/2023.wmt-1.47
%P 522-535

Markdown (Informal)

[Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation](https://aclanthology.org/2023.wmt-1.47) (Hirasawa et al., WMT 2023)

• Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation (Hirasawa et al., WMT 2023)

ACL

• Tosho Hirasawa, Emanuele Bugliarello, Desmond Elliott, and Mamoru Komachi. 2023. Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation. In Proceedings of the Eighth Conference on Machine Translation, pages 522–535, Singapore. Association for Computational Linguistics.