Argument Mining as a Text-to-Text Generation Task (2024)

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).

## introduction argument mining (am) is a form of discourse analysis that seeks to identify the structure of an argument within a text @xcite . this structure is typically represented through a dependency tree or a directed acyclic graph, as shown on the right-hand side of figure 1 . in the dependency tree, nodes correspond to text spans that contain arguments, which are then classified into specific argument types. the edges between the nodes represent the relations between the arguments. annotated corpora have been constructed for argument mining to reveal the argumentative struc-ture across various fields, such as student essays @xcite , biomedical research @xcite , and more. these corpora serve as the standard benchmark datasets, allowing for the performance evaluation of argument mining systems. given its practical applications in downstream tasks, such as text summarization @xcite and automatic essay scoring @xcite , argument mining has recently gained significant attention in discourse analysis. neural models enhanced the performance of argument mining, along with other natural language processing tasks. early models employed a pipeline approach involving three subtasks: identifying the argumentative text span, determining the argument type, and establishing the relation between the two arguments @xcite . however, recent models treat argument mining as dependency parsing and perform it in an end-to-end manner @xcite . these models are complex as they require separate mechanisms for each of the three tasks to be incorporated into the model. therefore, postprocessing is necessary to build valid dependency trees. furthermore, hyperparameter tuning poses difficulties in implementing these models. to tackle these difficulties, we exploited a simple text-to-text generation model with the translation between augmented natural languages (tanl) @xcite , which has achieved state-of-the-art performance on sentencelevel structured prediction tasks such as relation extraction, named entity recognition, and semantic role labeling. implementing tanl into am, we offer significant advantages: (1) a simple architecture that eliminates complex postprocessing and hyperparameter tuning, (2) the ability to adapt to various annotations based on the dataset, and (3) the potential to use recent large language models. ## proposed methods figure 1 provides an overview of our approach based on tanl @xcite . to obtain the argumentatively annotated text for tanl, we align the original text with the given argumentative spans, their types, and relations. then, we fine-tune t5 with the tanl framework using the annotated texts. ## conclusion in this paper, we introduced a simple yet strong approach for argument mining (am) using the tanl framework for text-to-text generation. to simplify and streamline annotations, we eliminated irrelevant text spans from the reference texts. experimental results obtained from aaec, abstrct, and cdcp demonstrated that our approach outperformed the current state-of-the-art method on these datasets. our research also indicated the efficacy of employing the tanl framework to predict document structures at the document level. furthermore, we found that removing irrelevant text spans decreased the inference time by approximately 30% on abstrct.