Year: 2025
Type: article
Abstract: The goal of argument mining, or AM, is to identify the argumentative structures in a document. Prior approaches necessitate a number of subtasks, including component classification, relation classification, and span identification. Therefore, rule-based postprocessing is required for these methods to extract generative structures from each subtask's output. This method increases the model's complexity and broadens the hyperparameter search space. We suggest a straightforward yet effective technique based on a text-to-text generation strategy employing a pretrained encoder-decoder language model to overcome this challenge. Our approach eliminates the requirement for task-specific postprocessing and hyperparameter optimisation by producing argumentatively annotated text for spans, components, and relations all at once. Additionally, as it is a simple text-to-text creation method, we may readily modify our strategy to fit different kinds of argumentative frameworks. Experimental findings show that our strategy works well, achieving state-of-the-art performance on three distinct benchmark datasets: the Cornell eRulemaking Cor- pus (CDCP), AbstRCT and the Argument-annotated Essays Corpus (AAEC). Keywords: Argument Mining, Text-to-Text Generation, T5
DOI: 10.55041/ijsrem47073
Author: Rudrendra Bahadur Singh
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410157491