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        <title>Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers</title>
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            <title>Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)</title>
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            <namePart type="given">Yamen</namePart>
            <namePart type="family">Ajjour</namePart>
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            <namePart type="given">Roy</namePart>
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            <namePart type="given">Roxanne</namePart>
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            <namePart type="given">Zhexiong</namePart>
            <namePart type="family">Liu</namePart>
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            <namePart type="given">Gabriella</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers
%A Saha, Sougata
%A Srihari, Rohini
%Y Ajjour, Yamen
%Y Bar-Haim, Roy
%Y El Baff, Roxanne
%Y Liu, Zhexiong
%Y Skitalinskaya, Gabriella
%S Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F saha-srihari-2024-turiya
%X 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.
%U https://aclanthology.org/2024.argmining-1.13
%P 124-129

Markdown (Informal)

[Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers](https://aclanthology.org/2024.argmining-1.13) (Saha & Srihari, ArgMining 2024)

• Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers (Saha & Srihari, ArgMining 2024)

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.