X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and
  Few-shot Agents
Mehrad Moradshahi Tianhao Shen Kalika Bali Monojit Choudhury Gaël de Chalendar Anmol Goel Sungkyun Kim Prashant Kodali Ponnurangam Kumaraguru Nasredine Semmar Sina J. Semnani Jiwon Seo Vivek Seshadri Manish Shrivastava Michael Sun Aditya Yadavalli Chaobin You Deyi Xiong Monica S. Lam
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.