@inproceedings{littek-lawrence-2026-scalable,
title = "Scalable Methods for Long-Range Argumentation in Distributed Discourse",
author = "Littek, Alina and
Lawrence, John",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.103/",
pages = "2042--2056",
ISBN = "979-8-89176-444-6",
abstract = "Argumentative reasoning over long or distributed discourse remains challenging because the number of possible relations between textual units grows quadratically, while true argumentative links remain sparse. As a result, exhaustive relation classification becomes computationally impractical for long documents, multi-document collections, and fragmented discourse. We address this problem with a modular graph-based architecture designed to aggressively reduce the candidate relation space prior to classification. The framework encodes graph-structured discourse representations and applies a two-stage unsupervised filtering pipeline to eliminate unlikely argumentative links before downstream inference. Across experimental settings, the proposed approach removes more than 99{\%} of candidate relations while substantially improving classification performance (up to 6.5{\texttimes} MCC over an unfiltered baseline). These results show that lightweight modular filtering can make large-scale argumentative reasoning possible without sacrificing predictive quality, enabling practical deployment in long-context and distributed-document environments."
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<abstract>Argumentative reasoning over long or distributed discourse remains challenging because the number of possible relations between textual units grows quadratically, while true argumentative links remain sparse. As a result, exhaustive relation classification becomes computationally impractical for long documents, multi-document collections, and fragmented discourse. We address this problem with a modular graph-based architecture designed to aggressively reduce the candidate relation space prior to classification. The framework encodes graph-structured discourse representations and applies a two-stage unsupervised filtering pipeline to eliminate unlikely argumentative links before downstream inference. Across experimental settings, the proposed approach removes more than 99% of candidate relations while substantially improving classification performance (up to 6.5× MCC over an unfiltered baseline). These results show that lightweight modular filtering can make large-scale argumentative reasoning possible without sacrificing predictive quality, enabling practical deployment in long-context and distributed-document environments.</abstract>
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%0 Conference Proceedings
%T Scalable Methods for Long-Range Argumentation in Distributed Discourse
%A Littek, Alina
%A Lawrence, John
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F littek-lawrence-2026-scalable
%X Argumentative reasoning over long or distributed discourse remains challenging because the number of possible relations between textual units grows quadratically, while true argumentative links remain sparse. As a result, exhaustive relation classification becomes computationally impractical for long documents, multi-document collections, and fragmented discourse. We address this problem with a modular graph-based architecture designed to aggressively reduce the candidate relation space prior to classification. The framework encodes graph-structured discourse representations and applies a two-stage unsupervised filtering pipeline to eliminate unlikely argumentative links before downstream inference. Across experimental settings, the proposed approach removes more than 99% of candidate relations while substantially improving classification performance (up to 6.5× MCC over an unfiltered baseline). These results show that lightweight modular filtering can make large-scale argumentative reasoning possible without sacrificing predictive quality, enabling practical deployment in long-context and distributed-document environments.
%U https://aclanthology.org/2026.findings-aacl.103/
%P 2042-2056
Markdown (Informal)
[Scalable Methods for Long-Range Argumentation in Distributed Discourse](https://aclanthology.org/2026.findings-aacl.103/) (Littek & Lawrence, Findings 2026)
ACL