@inproceedings{saha-srihari-2024-turiya,
    title = "Turiya at {D}ial{AM}-2024: Inference Anchoring Theory Based {LLM} Parsers",
    author = "Saha, Sougata  and
      Srihari, Rohini",
    editor = "Ajjour, Yamen  and
      Bar-Haim, Roy  and
      El Baff, Roxanne  and
      Liu, Zhexiong  and
      Skitalinskaya, Gabriella",
    booktitle = "Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.argmining-1.13/",
    doi = "10.18653/v1/2024.argmining-1.13",
    pages = "124--129",
    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."
}