AQE : Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach (2023)

Abstract: Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, each subtask alone is insufficient for a thorough understanding of the argumentative structure and reasoning process. To learn a complete view of an argument essay and capture the interdependence among argumentative components, we need to know what opinions people hold (i.e., claims), why those opinions are valid (i.e., supporting evidence), which source the evidence comes from (i.e., evidence type), and how those claims react to the debating topic (i.e., stance). In this work, we for the first time propose a challenging argument quadruplet extraction task (AQE), which can provide an all-in-one extraction of four argumentative components, i.e., claims, evidence, evidence types, and stances. To support this task, we construct a large-scale and challenging dataset. However, there is no existing method that can solve the argument quadruplet extraction. To fill this gap, we propose a novel quad-tagging augmented generative approach, which leverages a quadruplet tagging module to augment the training of the generative framework. The experimental results on our dataset demonstrate the empirical superiority of our proposed approach over several strong baselines.

## introduction the argument plays an important role in a wide range of human activities @xcite , from casual discussions @xcite @xcite to legal negotiations @xcite @xcite , document: sent 1: vaccines can save children's lives. sent 2: the american academy of pediatrics states that "most childhood vaccines are 90%-99% effective in preventing disease." sent 3: unicef estimates that $6.2 billion could be saved in treatment costs if vaccines were more prominent in the world's poorest countries. ## qam dataset to facilitate the study of the proposed argument quadruplet extraction (aqe) task, we create a fully annotated dataset based on the iam dataset @xcite . we first describe the background of the original iam dataset, followed by our data processing and human annotation details. ## task formulation more formally, given a document @xmath0, s 2 , . . . , s n ] with n sentences and its topic sentence s 0 , our task aims to extract the set of argumentative quadruplets from the document d, where s c k , s e @xmath1, @xmath2, . . . , n}) respectively denote the claim sentence and evidence sentence. a @xmath3, @xmath4, against} is the set for stance labels. t @xmath5. @xmath6, research, case, explanation, others} is the set of all evidence categories. ## model distinct from existing subtasks of argument mining, our argument quadruple extraction (aqe) task brings unique challenges to current methods. it requires not only good compatibility to accommodate each argument component well but also building up the shared modeling capacities that are conducive to each subtask. the emergence of pretrained generative model presents us with a good choice as a backbone framework to unify multiple targets into a general text-to-text learning paradigm. however, simply linearizing the argument quadruplets into a natural language sentence still can not fully exploit the underlying semantic dependencies among related components. to facilitate the task of argument quadruplet extraction, we propose an integrated framework augmented by a novel quadtagging approach. ## experiments 6.1 experimental settings the dataset is split randomly on the document level by a ratio of 8:1:1 for training, development and testing. the dataset statistics are shown in ## conclusions extensive experimental results and analysis validate the effectiveness of our proposed model.