Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets
<|reference_start|>Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets: 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.<|reference_end|>
@article{druart2024investigating,
    title={Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding
  Datasets},
    author={Lucas Druart (LIA), Valentin Vielzeuf, Yannick Est`eve (LIA)},
    journal={27th International Conference on Text, Speech and Dialogue, Sep
  2024, Brno (R{\'e}p. Tch{\`e}que), Czech Republic},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13269},
    primaryClass={cs.AI cs.CL cs.HC eess.SP}
}