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

• URL: http://arxiv.org/abs/2406.13269v1
• Date: Wed, 19 Jun 2024 06:59:57 GMT
• Title: Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets
• Authors: Lucas Druart, Valentin Vielzeuf, Yannick Estève,
• Abstract summary: In spoken Task-Oriented Dialogue (TOD) systems, the choice of the semantic representation describing the users' requests is key to a smooth interaction. This paper provides insights into automatic enhancement of spoken dialogue datasets' semantic representations.
• Score: 9.78470355087662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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