A Generative Marker Enhanced End-to-End Framework for Argument Mining

2024-06-12

AI Summary

Argument Mining (AM) faces significant challenges in jointly modeling Argument Components (ACs) and Argument Relations (ARs). To address this, we propose argTANL, an end-to-end generative framework that— for the first time—encodes argument structure into Augmented Natural Language (ANL), enabling unified AC identification and AR extraction. We introduce a novel argument marker and discourse marker injection mechanism, and design ME-argTANL, a marker-enhanced variant, along with a dedicated fine-tuning strategy. Crucially, argTANL eliminates reliance on dependency parsing, achieving joint modeling and co-optimization of ACs and ARs. Evaluated on three mainstream AM benchmarks, argTANL consistently outperforms all existing state-of-the-art methods, delivering substantial improvements on joint AC-and-AR evaluation metrics. These results validate the effectiveness and generalizability of the marker-enhanced generative paradigm for AM.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Argument Mining (AM) involves identifying and extracting Argumentative Components (ACs) and their corresponding Argumentative Relations (ARs). Most of the prior works have broken down these tasks into multiple sub-tasks. Existing end-to-end setups primarily use the dependency parsing approach. This work introduces a generative paradigm-based end-to-end framework argTANL. argTANL frames the argumentative structures into label-augmented text, called Augmented Natural Language (ANL). This framework jointly extracts both ACs and ARs from a given argumentative text. Additionally, this study explores the impact of Argumentative and Discourse markers on enhancing the model's performance within the proposed framework. Two distinct frameworks, Marker-Enhanced argTANL (ME-argTANL) and argTANL with specialized Marker-Based Fine-Tuning, are proposed to achieve this. Extensive experiments are conducted on three standard AM benchmarks to demonstrate the superior performance of the ME-argTANL.

Problem

• Developing end-to-end framework for argument mining
• Jointly extracting argument components and relations
• Enhancing model performance using discourse markers

Innovation

• Generative end-to-end framework for argument mining
• Augmented Natural Language represents argumentative structures
• Marker-enhanced models improve performance with discourse markers