Argument Mining is defined as the task of automatically identifying and
extracting argumentative components (e.g., premises, claims, etc.) and
detecting the existing relations among them (i.e., support, attack, rephrase,
no relation). One of the main issues when approaching this problem is the lack
of data, and the size of the publicly available corpora. In this work, we use
the recently annotated US2016 debate corpus. US2016 is the largest existing
argument annotated corpus, which allows exploring the benefits of the most
recent advances in Natural Language Processing in a complex domain like
Argument (relation) Mining. We present an exhaustive analysis of the behavior
of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT and ALBERT)
when predicting argument relations. Finally, we evaluate the models in five
different domains, with the objective of finding the less domain dependent
model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus,
and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus.

该研究使用了 US2016 辩论语料库来训练基于 Transformer 模型的种类，以推测不同参数下论据的关系，最终在五个不同领域中评价模型的性能，得出了极高的 F1 得分，并确定了相对不依赖特定领域的模型。