Summary of Multilingual Transfer and Domain Adaptation For Low-resource Languages Of Spain, by Yuanchang Luo et al.
Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain
by Yuanchang Luo, Zhanglin Wu, Daimeng Wei, Hengchao Shang, Zongyao Li, Jiaxin Guo, Zhiqiang Rao, Shaojun Li, Jinlong Yang, Yuhao Xie, Jiawei Zheng Bin Wei, Hao Yang
First submitted to arxiv on: 24 Sep 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 presents Huawei Translation Service Center’s participation in the WMT 2024 task, specifically translating Spanish texts into three low-resource languages: Aragonese, Aranese, and Asturian. The authors utilize various training strategies, including multilingual transfer, regularized dropout, forward translation, back translation, LabSE denoising, and transduction ensemble learning, to enhance their neural machine translation (NMT) model based on the deep transformer-big architecture. By applying these strategies, the submission achieves a competitive result in the final evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about translating Spanish texts into three languages that don’t have many resources: Aragonese, Aranese, and Asturian. The researchers used special techniques to improve their translation model, like using multiple languages together and trying out different methods. They also used a powerful AI model called the deep transformer-big architecture. By doing all this, they got good results in the competition. |
Keywords
» Artificial intelligence » Dropout » Transformer » Translation
