Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets - Archive ouverte HAL Access content directly
Conference Papers Year : 2024

Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets

Valentin Vielzeuf
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Yannick Estève

Abstract

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.
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hal-04616105 , version 1 (19-06-2024)

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  • HAL Id : hal-04616105 , version 1

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Lucas Druart, Valentin Vielzeuf, Yannick Estève. Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets. 27th International Conference on Text, Speech and Dialogue, Sep 2024, Brno (Rép. Tchèque), Czech Republic. ⟨hal-04616105⟩

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