基于大纲生成的跨语言对话数据集创建
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation
AI总结：
针对多语言任务型对话数据集的局限，提出基于大纲的标注方法创建大规模跨语言对话数据集COD，提升了数据质量并避免了性能虚高。
摘要：
多语言任务型对话（ToD）促进了为许多（社区）使用者获取服务和信息的能力。然而，这项技术的潜力尚未完全实现，因为当前用于多语言ToD的数据集——无论是用于模块化建模还是端到端建模——都存在严重的局限性。1）从零开始创建的数据集通常规模较小，无法覆盖许多可能的对话流程。2）基于翻译的ToD数据集在目标语言中可能缺乏自然性和文化特异性。在这项工作中，为了解决这些局限性，我们提出了一种新颖的基于大纲的多语言ToD数据集标注过程，其中特定领域的抽象对话模式被映射为自然语言大纲。这些大纲反过来通过提供关于每一轮意图和槽位的指令，指导目标语言标注员编写对话。通过这一过程，我们标注了一个新的大规模数据集，用于训练和评估多语言和跨语言ToD系统。我们的跨语言基于大纲的对话数据集（称为COD）支持4种不同语言的自然语言理解、对话状态跟踪以及端到端对话建模和评估：阿拉伯语、印尼语、俄语和斯瓦希里语。对COD与等效的基于翻译的数据集进行的定性和定量分析表明，基于大纲的方法提升了数据质量。最后，我们为跨语言ToD的一系列最先进系统设定了基准，为未来的工作设定了参考分数，并证明COD避免了先前基于翻译的ToD数据集中常见的性能过度虚高现象。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
