MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain
This work addresses the need for structured processing of scholarly documents in materials science, but it is incremental as it adapts existing argumentative zoning concepts to a new domain.
The authors tackled the problem of classifying argumentative zones in materials science publications by creating a new dataset of 50 annotated articles, showing that domain-specific pre-trained transformers achieve high classification performance, with transferability of categories from other domains varying.
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.