Turiya at D ial AM -2024: Inference Anchoring Theory Based LLM Parsers (2024)

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.

## introduction argumentation is prevalent in our daily verbal communication and represents chains of thought patterns and reasoning, making it an integral mode of persuasion @xcite . although argument mining (am) @xcite @xcite @xcite @xcite @xcite @xcite from monologues is well studied, formal models for parsing dialogues are lacking @xcite . @xcite introduced the first shared task in dialogue argument mining, where argumentation and dialogue information are modeled jointly in the domainindependent iat framework @xcite @xcite . the framework represents dialogues as a graph where the nodes comprise (i) locutions (l-nodes)-the argumentative discourse units (adus) from each speaker turn. (ii) propositions (i-nodes)-reconstructed l-nodes with resolved anaphora, pronouns, and deixis, making them independently coherent. the edges comprise (i) default transitions (tas) between l-nodes. (ii) s-nodes that connect propositions (i-nodes) and can be of types ra (default inference), ma (default rephrase), or ca (default conflict). (iii) ya-nodes that connect l-nodes with i-nodes, tas with s-nodes, or tas with i-nodes. here, we compare generative approaches against classification-based approaches for implementing the iat framework. since llms @xcite @xcite attain superior results on several tasks, we test their utility in dialogical argument mining and compare them against a biaffine-parsing-based implementation @xcite . we ask the following research questions: (i) can llms be used for parsing dialogues in the iat framework? we experiment with mistral-7b-instruct-v0.2 @xcite and present dialam as a generative task where the l-nodes, i-nodes, and ta-nodes are the context of the llm, and the task comprises determining the propositional (task a) and illocutionary (task b) relations. (ii) how do llms compare against simpler classificationbased dialogue parsers? we compare the llm parser against a biaffine-parsing-based parser that predicts the relationship and type between nodes. ## conclusion here, we computationally implement the theoretical iat framework using classification and llmbased models. we question the viability of leveraging llms, which are generative models, for such a nuanced task and compare them against simpler classifiers (non-generative) such as biaffine parsers. our results indicate that posing the graph construction problem as a generative task and finetuning llms outperforms biaffine classifiers. furthermore, ensembling the generative and classificationbased approaches yields the best results.