Scientific publications follow conventionalized rhetorical structures.
Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence
states a Motivation, a Result or Background information, has been proposed to
improve processing of scholarly documents. In this work, we adapt and extend
this idea to the domain of materials science research. We present and release a
new dataset of 50 manually annotated research articles. The dataset spans seven
sub-topics and is annotated with a materials-science focused multi-label
annotation scheme for AZ. We detail corpus statistics and demonstrate high
inter-annotator agreement. Our computational experiments show that using
domain-specific pre-trained transformer-based text encoders is key to high
classification performance. We also find that AZ categories from existing
datasets in other domains are transferable to varying degrees.

本研究基于科学出版物的常规修辞结构，将辩证区域的分类扩展到材料科学研究领域，并提出并发布了一个新的数据集，该数据集具有材料科学为中心的多标签注释方案，实验结果表明，使用特定领域的预训练基于 transformer 的文本编码器对高分类性能至关重要。