In spoken Task-Oriented Dialogue (TOD) systems, the choice of the semantic representation describing the users' requests is key to a smooth interaction. Indeed, the system uses this representation to reason over a database and its domain knowledge to choose its next action. The dialogue course thus depends on the information provided by this semantic representation. While textual datasets provide fine-grained semantic representations, spoken dialogue datasets fall behind. This paper provides insights into automatic enhancement of spoken dialogue datasets' semantic representations. Our contributions are three fold: (1) assess the relevance of Large Language Model fine-tuning, (2) evaluate the knowledge captured by the produced annotations and (3) highlight semi-automatic annotation implications.

翻译：在面向任务的口语对话系统中，用于描述用户请求的语义表示的选择是实现流畅交互的关键。系统利用该语义表示对数据库及其领域知识进行推理，从而选择下一步动作。因此，对话进程取决于该语义表示所提供的信息。尽管文本数据集能提供细粒度的语义表示，但口语对话数据集在此方面仍显不足。本文深入探讨了口语对话数据集语义表示的自动增强方法。我们的贡献主要体现在三个方面：(1) 评估大型语言模型微调的相关性，(2) 分析所生成标注所捕获的知识质量，以及 (3) 阐明半自动标注的实际影响。