The main limiting factor in the development of robust multilingual dialogue
evaluation metrics is the lack of multilingual data and the limited
availability of open sourced multilingual dialogue systems. In this work, we
propose a workaround for this lack of data by leveraging a strong multilingual
pretrained LLM and augmenting existing English dialogue data using Machine
Translation. We empirically show that the naive approach of finetuning a
pretrained multilingual encoder model with translated data is insufficient to
outperform the strong baseline of finetuning a multilingual model with only
source data. Instead, the best approach consists in the careful curation of
translated data using MT Quality Estimation metrics, excluding low quality
translations that hinder its performance.

通过使用机器翻译来增强现有的英文对话数据，我们提出了一个绕过数据缺乏问题的方法，用于开发鲁棒的多语言对话评估指标，并通过实验证明，与仅使用源数据微调多语言模型的强基线相比，仅仅使用翻译数据微调预训练的多语言编码器模型的天真方法无法取得更好的性能。相反，最佳方法是通过使用机器翻译质量估计度量仔细策划翻译数据，排除低质量翻译对其性能的影响。