Summary of M3p: Towards Multimodal Multilingual Translation with Multimodal Prompt, by Jian Yang et al.
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
by Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, Linzheng Cahi, Liqun Yang, Zhoujun Li
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a framework for multilingual machine translation that leverages visual context as a universal language-independent representation to facilitate translation between multiple languages. The Multimodal Multilingual neural Machine Translation (m3P) model aligns representations of different languages with the same meaning and generates conditional vision-language memory for translation. The authors construct a multilingual multimodal instruction dataset (InstrMulti102) supporting 102 languages. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us translate between many languages using pictures as a guide. It uses a special kind of machine learning called Multimodal Multilingual neural Machine Translation (m3P) to make translations better. The authors made a big dataset with instructions in 102 different languages. They tested their idea and it worked really well, beating other methods that only used text. |
Keywords
* Artificial intelligence * Machine learning * Translation
