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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2406.13269 |
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| _version_ | 1866911926250897408 |
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| author | Druart, Lucas Vielzeuf, Valentin Estève, Yannick |
| author_facet | Druart, Lucas Vielzeuf, Valentin Estève, Yannick |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13269 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets Druart, Lucas Vielzeuf, Valentin Estève, Yannick Artificial Intelligence Computation and Language Human-Computer Interaction Signal Processing 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. |
| title | Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2406.13269 |