Practical Solutions to Practical Problems in Developing Argument Mining Systems

TY - GEN

T1 - Practical Solutions to Practical Problems in Developing Argument Mining Systems

AU - Gemechu, Debela

AU - Ruiz-Dolz, Ramon

AU - Lawrence, John

AU - Reed, Chris

N1 - © 2025 Association for Computational Linguistics

PY - 2025/7

Y1 - 2025/7

N2 - The Open Argument Mining Framework (oAMF) addresses key challenges in argument mining research which still persist despite the field’s impressive growth. Researchers often face difficulties with cross-system comparisons, incompatible representation languages, and limited access to reusable tools. The oAMF introduces a standardised yet flexible architecture that enables seamless component benchmarking, rapid pipeline prototyping using elements from diverse research traditions, and unified evaluation methodologies that preserve theoretical compatibility. By reducing technical overhead, the framework allows researchers to focus on advancing core argument mining capabilities rather than reimplementing infrastructure, fostering greater collaboration at a time when computational reasoning is increasingly vital in the era of large language models.

AB - The Open Argument Mining Framework (oAMF) addresses key challenges in argument mining research which still persist despite the field’s impressive growth. Researchers often face difficulties with cross-system comparisons, incompatible representation languages, and limited access to reusable tools. The oAMF introduces a standardised yet flexible architecture that enables seamless component benchmarking, rapid pipeline prototyping using elements from diverse research traditions, and unified evaluation methodologies that preserve theoretical compatibility. By reducing technical overhead, the framework allows researchers to focus on advancing core argument mining capabilities rather than reimplementing infrastructure, fostering greater collaboration at a time when computational reasoning is increasingly vital in the era of large language models.

UR - https://doi.org/10.18653/v1/2025.argmining-1

U2 - 10.18653/v1/2025.argmining-1.9

DO - 10.18653/v1/2025.argmining-1.9

M3 - Conference contribution

SN - 979-8-89176-258-9

SP - 100

EP - 106

BT - Proceedings of the 12th Argument mining Workshop

A2 - Chistova, Elena

A2 - Cimiano, Philipp

A2 - Haddadan, Shohreh

A2 - Lapesa, Gabriella

A2 - Ruiz-Dolz, Ramon

PB - Association for Computational Linguistics

CY - Texas, USA

T2 - The 12th Workshop on Argument Mining

Y2 - 31 July 2025 through 31 July 2025

ER -