Task-oriented dialogue (ToD) systems have been mostly created for high-resource languages, such as English and Chinese. However, there is a need to develop ToD systems for other regional or local languages to broaden their ability to comprehend the dialogue contexts in various languages. This paper introduces IndoToD, an end-to-end multi domain ToD benchmark in Indonesian. We extend two English ToD datasets to Indonesian, comprising four different domains by delexicalization to efficiently reduce the size of annotations. To ensure a high-quality data collection, we hire native speakers to manually translate the dialogues. Along with the original English datasets, these new Indonesian datasets serve as an effective benchmark for evaluating Indonesian and English ToD systems as well as exploring the potential benefits of cross-lingual and bilingual transfer learning approaches.

这篇论文介绍了一个名为IndoToD的端到端多领域任务导向对话系统基准，用于印尼语。作者通过将两个英语ToD数据集转换为印尼语，并通过去成分化来减小注解的规模，提供了一个高质量的数据集。这个基准可以用于评估印尼语和英语ToD系统，同时探索跨语言和双语传递学习方法的潜在益处。

IndoToD: 一个用于端到端任务导向对话系统的多领域印尼语基准数据集