  Task-oriented dialogue research has mainly focused on a few popular languages
like English and Chinese, due to the high dataset creation cost for a new
language. To reduce the cost, we apply manual editing to automatically
translated data. We create a new multilingual benchmark, X-RiSAWOZ, by
translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean;
and a code-mixed English-Hindi language. X-RiSAWOZ has more than 18,000
human-verified dialogue utterances for each language, and unlike most
multilingual prior work, is an end-to-end dataset for building
fully-functioning agents.
  The many difficulties we encountered in creating X-RiSAWOZ led us to develop
a toolset to accelerate the post-editing of a new language dataset after
translation. This toolset improves machine translation with a hybrid entity
alignment technique that combines neural with dictionary-based methods, along
with many automated and semi-automated validation checks.
  We establish strong baselines for X-RiSAWOZ by training dialogue agents in
the zero- and few-shot settings where limited gold data is available in the
target language. Our results suggest that our translation and post-editing
methodology and toolset can be used to create new high-quality multilingual
dialogue agents cost-effectively. Our dataset, code, and toolkit are released
open-source.
