AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach
This work addresses the need for a holistic understanding of argumentative structures in natural language processing, though it is incremental as it builds upon existing argument mining subtasks.
The authors tackled the problem of comprehensively extracting argumentative components by proposing the novel argument quadruplet extraction (AQE) task, which simultaneously identifies claims, evidence, evidence types, and stances, and their approach achieved empirical superiority over strong baselines on a newly constructed dataset.
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.