大规模多语言神经机器翻译鲁棒性的实证研究
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation
AI总结：
本研究针对印尼语-汉语翻译的鲁棒性展开实证研究，构建了对应评估基准数据集，借助不同规模NLLB-200模型测试并结合自动与人工评估，揭示了噪声与错误的关联及评估指标的关系，公开了数据集。
摘要：
大规模多语言神经机器翻译（MMNMT）已被证实能够提升低资源语言的翻译质量。本文针对印尼语-汉语翻译在面对各类自然出现的噪声时的翻译鲁棒性展开实证研究。为评估该鲁棒性，我们构建了一个印尼语-汉语翻译鲁棒性评估基准数据集。我们使用四种不同规模的NLLB-200模型将该数据集自动翻译成汉语，并开展了自动评估与人工评估。我们的深入分析揭示了翻译错误类型与存在的噪声类型之间的相关性、这些相关性在不同模型规模下的变化情况，以及自动评估指标与人工评估指标之间的关系。该数据集已在https://github.com/tjunlp-lab/ID-ZH-MTRobustEval 公开。
英文摘要：
Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages. In this paper, we empirically investigate the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. To assess this, we create a robustness evaluation benchmark dataset for Indonesian-Chinese translation. This dataset is automatically translated into Chinese using four NLLB-200 models of different sizes. We conduct both automatic and human evaluations. Our in-depth analysis reveal the correlations between translation error types and the types of noise present, how these correlations change across different model sizes, and the relationships between automatic evaluation indicators and human evaluation indicators. The dataset is publicly available at https://github.com/tjunlp-lab/ID-ZH-MTRobustEval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
