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.

该研究提出了一个新的任务 - argument quadruplet extraction， 旨在提供四种互相关联的论证组成部分提取，即声明、证据、证据类型和立场，并通过构建一个数据集，并提出了一种基于生成模型的新方法来解决任务。