Argument Mining as a Text-to-Text Generation Task

Abstract

Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures from the output of each subtask. This approach adds to the complexity of the model and expands the search space of the hyperparameters. To address this difficulty, we propose a simple yet strong method based on a text-to-text generation approach using a pretrained encoder-decoder language model. Our method simultaneously generates argumentatively annotated text for spans, components, and relations, eliminating the need for task-specific postprocessing and hyperparameter tuning. Furthermore, because it is a straightforward text-to-text generation method, we can easily adapt our approach to various types of argumentative structures. Experimental results demonstrate the effectiveness of our method, as it achieves state-of-the-art performance on three different types of benchmark datasets: the Argument-annotated Essays Corpus(AAEC), AbstRCT, and the Cornell eRulemaking Corpus(CDCP)

Figures (3)

• Figure 1: Overview of our methods. For our methodology, we input text into a pretrained encoder-decoder, such as T5 and FLAN T5. This process generates an argumentatively annotated text with spans, components, and relations. We then postprocess the output text to extract the argumentative structure.
• Figure 2: Comparison of inference time with and without nonargumentative spans in AbstRCT. We set the batch size to 2 for all models during inference and measured the time required to complete the process on the entire test dataset.
• Figure 3: Comparison of F1 score output by label for the Component and Relation tasks.