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English(EN) Transformer-Encoder Trees for Efficient Multilingual Machine Translation and Speech Translation

新的 Transformer 编码器树架构提高了多语言翻译效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为 Transformer 编码器树 (TET) 的新架构，以改进多语言机器翻译和语音翻译。这种分层、非自回归模型在相似目标语言之间共享中间表示，提高了低资源语言的准确性并减少了计算冗余。TET 允许所有目标语言在一次传递中生成，消除了传统自回归模型的顺序瓶颈，并实现了并行解码。该架构已证明在参数数量和推理计算方面有显著减少，在语音翻译方面，它提供了具有显著更快推理时间的竞争性质量。 AI

影响 引入了一种更高效的多语言翻译架构，有望加快翻译服务速度并提高低资源语言的质量。

排序理由 介绍新模型架构及其性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的 Transformer 编码器树架构提高了多语言翻译效率

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介绍新模型架构及其性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yiwen Guan, Jacob Whitehill ·

    用于高效多语言机器翻译和语音翻译的 Transformer-Encoder 树

    arXiv:2509.17930v3 Announce Type: replace-cross Abstract: Multilingual translation suffers from computational redundancy, especially when translating into multiple languages simultaneously. In addition, translation quality can suffer for low-resource languages. To address this, w…

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