MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science
  Domain
Timo Pierre Schrader Teresa Bürkle Sophie Henning Sherry Tan Matteo Finco Stefan Grünewald Maira Indrikova Felix Hildebrand Annemarie Friedrich
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.