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Communication Dans Un Congrès Année : 2023

X-RiSAWOZ: High-quality end-to-end multilingual dialogue datasets and few-shot agents

Résumé

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 EnglishHindi 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.
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Dates et versions

cea-04532045 , version 1 (04-04-2024)

Identifiants

Citer

Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gaël de Chalendar, et al.. X-RiSAWOZ: High-quality end-to-end multilingual dialogue datasets and few-shot agents. ACL 2023 - The 61st Annual Meeting of the Association for Computational Linguistics, Jul 2023, Toronto, Canada. pp.2773-2794, ⟨10.18653/v1/2023.findings-acl.174⟩. ⟨cea-04532045⟩
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