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English(EN) In the Blind: Building Pseudo-References for MT Evaluation

新方法为机器翻译评估创建伪参考

研究人员开发了一种新颖的方法来为机器翻译评估创建伪参考,特别是对于缺乏人工生成参考的语言对。该方法使用多个机器翻译模型和质量估算模型来选择最佳翻译,并由GPT-5.5对选定的输出进行后编辑。该系统包含一个对流畅但错误的语言识别的惩罚机制,该机制成功消除了错误语言的输出,并与基线方法相比提高了选择分数。 AI

影响 这种生成评估参考的新方法可以提高机器翻译系统的准确性和可靠性,特别是对于资源匮乏的语言。

排序理由 该条目是一篇学术论文,详细介绍了机器翻译评估的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法为机器翻译评估创建伪参考

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该条目是一篇学术论文,详细介绍了机器翻译评估的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Diptesh Kanojia, Chi-kiu Lo, Archchana Sindhujan, Samuel Larkin, Greg Hanneman, Alon Lavie ·

    盲测:为机器翻译评估构建伪参考

    arXiv:2609.13611v1 Announce Type: new Abstract: The WMT26 General MT task evaluates systems on 10 language pairs that have no human references (neither translated from scratch nor post-edited from MT output by humans). We describe how we built the pseudo-references for these pair…