Scholarly Argumentation Mining (SAM) has recently gained attention due to its
potential to help scholars with the rapid growth of published scientific
literature. It comprises two subtasks: argumentative discourse unit recognition
(ADUR) and argumentative relation extraction (ARE), both of which are
challenging since they require e.g. the integration of domain knowledge, the
detection of implicit statements, and the disambiguation of argument structure.
While previous work focused on dataset construction and baseline methods for
specific document sections, such as abstract or results, full-text scholarly
argumentation mining has seen little progress. In this work, we introduce a
sequential pipeline model combining ADUR and ARE for full-text SAM, and provide
a first analysis of the performance of pretrained language models (PLMs) on
both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus,
outperforming the previous best reported result by a large margin (+7% F1). We
also present the first results for ARE, and thus for the full AM pipeline, on
this benchmark dataset. Our detailed error analysis reveals that non-contiguous
ADUs as well as the interpretation of discourse connectors pose major
challenges and that data annotation needs to be more consistent.

通过序列管道模型将学术论证挖掘中的 argumentative discourse unit recognition 和 argumentative relation extraction 组合起来， 并提出了预训练语言模型用于学术文本挖掘的方法，取得了新的最佳表现，并对数据注释方面的一致性进行了分析。