@inproceedings{williams-etal-2026-amresources,
title = "{AMR}esources: Cataloging Argument Mining Datasets",
author = "Williams, Dexter and
Liu, Shiwei and
Stede, Manfred and
Wachsmuth, Henning and
Schneider, Jodi",
editor = "Elaraby, Mohamed and
Hautli-Janisz, Annette and
Romberg, Julia and
Musi, Elena and
Ruggeri, Federico and
Lawrence, John",
booktitle = "Proceedings of the 13th Workshop on Argument Mining and Reasoning",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.argmining-1.5/",
pages = "37--42",
ISBN = "979-8-89176-399-9",
abstract = "Annotated datasets are essential for developing and evaluating argument mining systems, yet information about argument mining datasets remains scattered across papers, repositories, and task-specific surveys. To address this, we introduce AMResources (http://purl.archive.org/amresources), an online catalog that organizes argument mining datasets by task, and captures relationships among datasets, releases, and papers. We draw particular attention to relationships such as re-annotation and dataset extension. To curate dataset information into a consistent and provenance-aware structure, AMResources links datasets to canonical papers. For each dataset release, AMResources records standardized metadata such as language, genre, unit type and unit count, annotator characteristics, agreement reporting, and accessibility. We argue that such structured dataset documentation remains critical in the era of large language models, where annotated datasets increasingly serve as high-quality evaluation benchmarks and where tracing dataset provenance and annotation layers is necessary for systematic comparisons across tasks."
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%0 Conference Proceedings
%T AMResources: Cataloging Argument Mining Datasets
%A Williams, Dexter
%A Liu, Shiwei
%A Stede, Manfred
%A Wachsmuth, Henning
%A Schneider, Jodi
%Y Elaraby, Mohamed
%Y Hautli-Janisz, Annette
%Y Romberg, Julia
%Y Musi, Elena
%Y Ruggeri, Federico
%Y Lawrence, John
%S Proceedings of the 13th Workshop on Argument Mining and Reasoning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-399-9
%F williams-etal-2026-amresources
%X Annotated datasets are essential for developing and evaluating argument mining systems, yet information about argument mining datasets remains scattered across papers, repositories, and task-specific surveys. To address this, we introduce AMResources (http://purl.archive.org/amresources), an online catalog that organizes argument mining datasets by task, and captures relationships among datasets, releases, and papers. We draw particular attention to relationships such as re-annotation and dataset extension. To curate dataset information into a consistent and provenance-aware structure, AMResources links datasets to canonical papers. For each dataset release, AMResources records standardized metadata such as language, genre, unit type and unit count, annotator characteristics, agreement reporting, and accessibility. We argue that such structured dataset documentation remains critical in the era of large language models, where annotated datasets increasingly serve as high-quality evaluation benchmarks and where tracing dataset provenance and annotation layers is necessary for systematic comparisons across tasks.
%U https://aclanthology.org/2026.argmining-1.5/
%P 37-42
Markdown (Informal)
[AMResources: Cataloging Argument Mining Datasets](https://aclanthology.org/2026.argmining-1.5/) (Williams et al., ArgMining 2026)
ACL
- Dexter Williams, Shiwei Liu, Manfred Stede, Henning Wachsmuth, and Jodi Schneider. 2026. AMResources: Cataloging Argument Mining Datasets. In Proceedings of the 13th Workshop on Argument Mining and Reasoning, pages 37–42, San Diego, California, USA. Association for Computational Linguistics.