Abstract
In order to overcome challenges of traditional deliberation approaches that often silo information exchange between synchronous and asynchronous modes therefore hindering effective deliberation, we present a hybrid framework combining Large Language Models (LLMs) and human-in-the-loop curation to generate argument maps from deliberation transcripts. This approach aims to enhance the efficiency and quality of the generated argument maps, promote transparency, and connect the asynchronous and synchronous deliberation modes. Finally, we outline a realistic deliberation scenario where this process can be successfully integrated.- Anthology ID:
- 2024.delite-1.6
- Volume:
- Proceedings of the First Workshop on Language-driven Deliberation Technology (DELITE) @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Annette Hautli-Janisz, Gabriella Lapesa, Lucas Anastasiou, Valentin Gold, Anna De Liddo, Chris Reed
- Venue:
- DELITE
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 45–51
- Language:
- URL:
- https://aclanthology.org/2024.delite-1.6
- DOI:
- Cite (ACL):
- Lucas Anastasiou and Anna De Liddo. 2024. A Hybrid Human-AI Approach for Argument Map Creation From Transcripts. In Proceedings of the First Workshop on Language-driven Deliberation Technology (DELITE) @ LREC-COLING 2024, pages 45–51, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- A Hybrid Human-AI Approach for Argument Map Creation From Transcripts (Anastasiou & De Liddo, DELITE 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.delite-1.6.pdf