Research on (multi-domain) task-oriented dialog (TOD) has predominantly
focused on the English language, primarily due to the shortage of robust TOD
datasets in other languages, preventing the systematic investigation of
cross-lingual transfer for this crucial NLP application area. In this work, we
introduce Multi2WOZ, a new multilingual multi-domain TOD dataset, derived from
the well-established English dataset MultiWOZ, that spans four typologically
diverse languages: Chinese, German, Arabic, and Russian. In contrast to
concurrent efforts, Multi2WOZ contains gold-standard dialogs in target
languages that are directly comparable with development and test portions of
the English dataset, enabling reliable and comparative estimates of
cross-lingual transfer performance for TOD. We then introduce a new framework
for multilingual conversational specialization of pretrained language models
(PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream
TOD tasks. Using such conversational PrLMs specialized for concrete target
languages, we systematically benchmark a number of zero-shot and few-shot
cross-lingual transfer approaches on two standard TOD tasks: Dialog State
Tracking and Response Retrieval. Our experiments show that, in most setups, the
best performance entails the combination of (I) conversational specialization
in the target language and (ii) few-shot transfer for the concrete TOD task.
Most importantly, we show that our conversational specialization in the target
language allows for an exceptionally sample-efficient few-shot transfer for
downstream TOD tasks.

本文介绍了 Multi2WOZ 多语言多领域任务导向对话数据集，并提出了一种用于传输任意下游任务的多语言对话 PrLM 的新框架，证明了在多数情况下，最佳表现是将（I）针对目标语言的会话专业化与（ii）少次转移结合在一起，并且我们展示了针对目标语言的会话专业化可以实现下游任务的异常样本效率的少次转移。

Multi2WOZ: 一个强大的多语言数据集和面向任务型对话的预训练

Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining  for Task-Oriented Dialog

This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9).
This edition of the DSTC focuses on applying end-to-end dialog technologies for
four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling
with unstructured knowledge access, 2. Multi-domain task-oriented dialog, 3.
Interactive evaluation of dialog, and 4. Situated interactive multi-modal
dialog. This paper describes the task definition, provided datasets, baselines
and evaluation set-up for each track. We also summarize the results of the
submitted systems to highlight the overall trends of the state-of-the-art
technologies for the tasks.

介绍了第九届 Dialog System Technology Challenge (DSTC-9)，重点介绍使用端到端对话技术的四项任务，包括 1. 带有非结构化知识访问的任务导向型对话建模，2. 多领域任务导向型对话，3. 对话的交互式评估，以及 4. 位置交互式多模对话。讨论了每个任务的任务定义、基线和评估设置，总结了提交系统的结果，以突出该领域技术的最新趋势。