m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914729415409664 |
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| author | Yang, Jian Guo, Hongcheng Yin, Yuwei Bai, Jiaqi Wang, Bing Liu, Jiaheng Liang, Xinnian Cahi, Linzheng Yang, Liqun Li, Zhoujun |
| author_facet | Yang, Jian Guo, Hongcheng Yin, Yuwei Bai, Jiaqi Wang, Bing Liu, Jiaheng Liang, Xinnian Cahi, Linzheng Yang, Liqun Li, Zhoujun |
| contents | Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between languages in the text-only modality, especially when the number of languages is large. To bridge this gap, we introduce visual context as the universal language-independent representation to facilitate multilingual translation. In this paper, we propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual neural Machine Translation (m3P), which aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. We construct a multilingual multimodal instruction dataset (InstrMulti102) to support 102 languages. Our method aims to minimize the representation distance of different languages by regarding the image as a central language. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin. Furthermore, the probing experiments validate the effectiveness of our method in enhancing translation under the low-resource and massively multilingual scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17556 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt Yang, Jian Guo, Hongcheng Yin, Yuwei Bai, Jiaqi Wang, Bing Liu, Jiaheng Liang, Xinnian Cahi, Linzheng Yang, Liqun Li, Zhoujun Computation and Language Artificial Intelligence Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between languages in the text-only modality, especially when the number of languages is large. To bridge this gap, we introduce visual context as the universal language-independent representation to facilitate multilingual translation. In this paper, we propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual neural Machine Translation (m3P), which aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. We construct a multilingual multimodal instruction dataset (InstrMulti102) to support 102 languages. Our method aims to minimize the representation distance of different languages by regarding the image as a central language. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin. Furthermore, the probing experiments validate the effectiveness of our method in enhancing translation under the low-resource and massively multilingual scenario. |
| title | m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17556 |