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 this https URL

我们通过实证研究了印度尼西亚 - 汉语翻译在不同噪声环境下的翻译稳健性，并创建了一个用于评估翻译稳健性的基准数据集。

大规模多语言神经机器翻译的鲁棒性实证研究

An Empirical Study on the Robustness of Massively Multilingual Neural  Machine Translation

The recently proposed massively multilingual neural machine translation (NMT)
system has been shown to be capable of translating over 100 languages to and
from English within a single model. Its improved translation performance on low
resource languages hints at potential cross-lingual transfer capability for
downstream tasks. In this paper, we evaluate the cross-lingual effectiveness of
representations from the encoder of a massively multilingual NMT model on 5
downstream classification and sequence labeling tasks covering a diverse set of
over 50 languages. We compare against a strong baseline, multilingual BERT
(mBERT), in different cross-lingual transfer learning scenarios and show gains
in zero-shot transfer in 4 out of these 5 tasks.

本文评估了一个大规模多语言神经机器翻译模型编码器在五个跨语言分类和序列标记任务中的跨语言效果，并展示了零 - shot 转移学习中在四个任务中的增益。