Multi3WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for
  Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems
Songbo Hu Han Zhou Mete Hergul Milan Gritta Guchun Zhang Ignacio Iacobacci Ivan Vulić Anna Korhonen
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.