arXiv 2311.049222023-11-03cs.CLcs.AIeess.ASeess.SP

级联对话状态跟踪模型在口语对话中是否在不恰当的时机发言？

Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?

Orange Innovation, France（法国Orange创新公司）

Lucas Druart, Léo Jacqmin, Benoît Favre, Lina Maria Rojas-Barahona, Valentin Vielzeuf

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AI总结：

本文分析了TOD系统中级联模型在复杂对话状态跟踪任务中的交互错误，发现非分类槽值错误是口语与文本对话系统差距的关键，并探索了改进转录及纠正错误的潜在方案。

摘要：

在任务型对话（TOD）系统中，正确更新系统对用户需求的理解是实现流畅交互的关键。传统上，TOD系统由多个相互交互的模块组成。尽管这些组件各自是活跃研究社区的焦点，但它们在交互中的行为却可能被忽视。本文提出对最先进系统在复杂环境（如高度依赖对话上下文的对话状态跟踪）中的错误进行全面分析。基于口语版MultiWoz，我们发现非分类槽值的错误是必须解决的，以弥合口语与基于文本的对话系统之间的差距。我们探索了改进转录并帮助对话状态跟踪生成模型纠正此类错误的潜在解决方案。

英文摘要：

In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction. Traditionally TOD systems are composed of several modules that interact with one another. While each of these components is the focus of active research communities, their behavior in interaction can be overlooked. This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking which highly depends on the dialogue context. Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems. We explore potential solutions to improve transcriptions and help dialogue state tracking generative models correct such errors.

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。