MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain
Timo Schrader, Teresa Bürkle, Sophie Henning, Sherry Tan, Matteo Finco, Stefan Grünewald, Maira Indrikova, Felix Hildebrand, Annemarie Friedrich
Abstract
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.- Anthology ID:
- 2023.codi-1.1
- Volume:
- Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Michael Strube, Chloe Braud, Christian Hardmeier, Junyi Jessy Li, Sharid Loaiciga, Amir Zeldes
- Venue:
- CODI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–15
- Language:
- URL:
- https://aclanthology.org/2023.codi-1.1
- DOI:
- 10.18653/v1/2023.codi-1.1
- Cite (ACL):
- Timo Schrader, Teresa Bürkle, Sophie Henning, Sherry Tan, Matteo Finco, Stefan Grünewald, Maira Indrikova, Felix Hildebrand, and Annemarie Friedrich. 2023. MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain. In Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023), pages 1–15, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain (Schrader et al., CODI 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.codi-1.1.pdf