Multilingual task-oriented dialogue (ToD) facilitates access to services and
information for many (communities of) speakers. Nevertheless, the potential of
this technology is not fully realised, as current datasets for multilingual ToD
- both for modular and end-to-end modelling - suffer from severe limitations.
1) When created from scratch, they are usually small in scale and fail to cover
many possible dialogue flows. 2) Translation-based ToD datasets might lack
naturalness and cultural specificity in the target language. In this work, to
tackle these limitations we propose a novel outline-based annotation process
for multilingual ToD datasets, where domain-specific abstract schemata of
dialogue are mapped into natural language outlines. These in turn guide the
target language annotators in writing a dialogue by providing instructions
about each turn's intents and slots. Through this process we annotate a new
large-scale dataset for training and evaluation of multilingual and
cross-lingual ToD systems. Our Cross-lingual Outline-based Dialogue dataset
(termed COD) enables natural language understanding, dialogue state tracking,
and end-to-end dialogue modelling and evaluation in 4 diverse languages:
Arabic, Indonesian, Russian, and Kiswahili. Qualitative and quantitative
analyses of COD versus an equivalent translation-based dataset demonstrate
improvements in data quality, unlocked by the outline-based approach. Finally,
we benchmark a series of state-of-the-art systems for cross-lingual ToD,
setting reference scores for future work and demonstrating that COD prevents
over-inflated performance, typically met with prior translation-based ToD
datasets.

本篇论文提出了一种基于大纲的多语言任务导向型对话数据集标注方法，使得多语言对话系统的数据集不再受到规模限制和翻译自然性以及文化特异性等方面的问题，设计出一组新的大规模数据集，其中的 Cross-lingual Outline-based Dialogue 数据集允许进行自然语言理解、对话状态跟踪和端到端对话建模和评估，并通过多种语言以及基于最先进技术的交叉语言对话系统对数据集进行了定量和定性分析，同时设定了参考分数以指导后续工作。