面向口语对话理解数据集的低成本LLM标注研究
Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets
AI总结：
针对口语对话数据集语义表征不足的问题，本文研究利用大语言模型自动增强其语义标注，评估了微调适用性、标注知识捕获能力及半自动标注的影响。
摘要：
在口语任务导向对话（TOD）系统中，描述用户请求的语义表征选择是实现流畅交互的关键。系统会利用该表征在数据库及其领域知识上进行推理，以选择下一步动作，因此对话进程取决于该语义表征提供的信息。文本数据集具备细粒度语义表征，但口语对话数据集在这方面存在差距。本文针对口语对话数据集语义表征的自动增强展开研究并提供见解。研究贡献分为三方面：（1）评估大语言模型微调的适用性；（2）衡量生成标注所捕获的知识水平；（3）阐明半自动标注的应用影响。
英文摘要：
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.
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。
