AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented
  Generative Approach
Jia Guo Liying Cheng Wenxuan Zhang Stanley Kok Xin Li Lidong Bing
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.