Creating high-quality annotated data for task-oriented dialog (ToD) is known
to be notoriously difficult, and the challenges are amplified when the goal is
to create equitable, culturally adapted, and large-scale ToD datasets for
multiple languages. Therefore, the current datasets are still very scarce and
suffer from limitations such as translation-based non-native dialogs with
translation artefacts, small scale, or lack of cultural adaptation, among
others. In this work, we first take stock of the current landscape of
multilingual ToD datasets, offering a systematic overview of their properties
and limitations. Aiming to reduce all the detected limitations, we then
introduce Multi3WOZ, a novel multilingual, multi-domain, multi-parallel ToD
dataset. It is large-scale and offers culturally adapted dialogs in 4 languages
to enable training and evaluation of multilingual and cross-lingual ToD
systems. We describe a complex bottom-up data collection process that yielded
the final dataset, and offer the first sets of baseline scores across different
ToD-related tasks for future reference, also highlighting its challenging
nature.

创造高质量的任务导向对话（ToD）的注释数据是困难的，特别是在为多种语言创造公平、文化适应性强、大规模的 ToD 数据集时面临诸多挑战。因此，现有的数据集仍然非常有限，并且存在诸多限制，如基于翻译的非母语对话、小规模或缺乏文化适应性等。在这项工作中，我们首先对多语言 ToD 数据集的现状进行了全面的梳理，提供了它们的特点和限制的系统概述。为了减少所有检测到的限制，我们推出了 Multi3WOZ，这是一个创新的多语言、多领域、多平行 ToD 数据集。它是大规模的，并且提供了四种语言的文化适应对话，以便训练和评估多语言和跨语言的 ToD 系统。我们描述了一个复杂的自下而上的数据收集过程，得到了最终的数据集，并提供了将来参考的不同 ToD 相关任务的基准分数，并突出了它具有挑战性的特点。