Abstract
Representing discourse as argument graphs facilitates robust analysis. Although computational frameworks for constructing graphs from monologues exist, there is a lack of frameworks for parsing dialogue. Inference Anchoring Theory (IAT) is a theoretical framework for extracting graphical argument structures and relationships from dialogues. Here, we introduce computational models for implementing the IAT framework for parsing dialogues. We experiment with a classification-based biaffine parser and Large Language Model (LLM)-based generative methods and compare them. Our results demonstrate the utility of finetuning LLMs for constructing IAT-based argument graphs from dialogues, which is a nuanced task.- Anthology ID:
- 2024.argmining-1.13
- Volume:
- Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Yamen Ajjour, Roy Bar-Haim, Roxanne El Baff, Zhexiong Liu, Gabriella Skitalinskaya
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 124–129
- Language:
- URL:
- https://aclanthology.org/2024.argmining-1.13
- DOI:
- 10.18653/v1/2024.argmining-1.13
- Cite (ACL):
- Sougata Saha and Rohini Srihari. 2024. Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 124–129, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers (Saha & Srihari, ArgMining 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.argmining-1.13.pdf