Argumentation is one of society's foundational pillars, and, sparked by
advances in NLP and the vast availability of text data, automated mining of
arguments receives increasing attention. A decisive property of arguments is
their strength or quality. While there are works on the automated estimation of
argument strength, their scope is narrow: they focus on isolated datasets and
neglect the interactions with related argument mining tasks, such as argument
identification, evidence detection, or emotional appeal. In this work, we close
this gap by approaching argument quality estimation from multiple different
angles: Grounded on rich results from thorough empirical evaluations, we assess
the generalization capabilities of argument quality estimation across diverse
domains, the interplay with related argument mining tasks, and the impact of
emotions on perceived argument strength. We find that generalization depends on
a sufficient representation of different domains in the training part. In
zero-shot transfer and multi-task experiments, we reveal that argument quality
is among the more challenging tasks but can improve others. Finally, we show
that emotions play a minor role in argument quality than is often assumed.

本文研究基于 NLP 和大量文本数据得到的自动化 argument mining，评估 argument 质量估计对于不同领域的通用性、与相关 argument mining 任务的相互作用以及情感对于观察到的 argument 强度的影响。作者发现不同领域的训练数据可以提高质量估计的泛化能力，在零样本迁移和多任务实验中，argument quality 在多个方面都能够得到改善，而情感对 argument quality 的影响不如人们普遍认为的那样大。