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Task-oriented dialogue research has mainly focused on a few popular languageslike English and Chinese, due to the high dataset creation cost for a newlanguage. To reduce the cost, we apply manual editing to automaticallytranslated data. We create a new multilingual benchmark, X-RiSAWOZ, bytranslating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean;and a code-mixed English-Hindi language. X-RiSAWOZ has more than 18,000human-verified dialogue utterances for each language, and unlike mostmultilingual prior work, is an end-to-end dataset for buildingfully-functioning agents.  The many difficulties we encountered in creating X-RiSAWOZ led us to developa toolset to accelerate the post-editing of a new language dataset aftertranslation. This toolset improves machine translation with a hybrid entityalignment technique that combines neural with dictionary-based methods, alongwith many automated and semi-automated validation checks.  We establish strong baselines for X-RiSAWOZ by training dialogue agents inthe zero- and few-shot settings where limited gold data is available in thetarget language. Our results suggest that our translation and post-editingmethodology and toolset can be used to create new high-quality multilingualdialogue agents cost-effectively. Our dataset, code, and toolkit are releasedopen-source.

X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents