@inproceedings{graiff-etal-2026-cross,
title = "Cross-lingual and cross-country approaches to argument component detection: a comparative study.",
author = "Graiff, Cecilia and
Clavel, Chlo{\'e} and
Sagot, Beno{\^i}t",
editor = "Chen, Pinzhen and
Zouhar, Vil{\'e}m and
Hu, Hanxu and
Khanuja, Simran and
Zhu, Wenhao and
Haddow, Barry and
Birch, Alexandra and
Aji, Alham Fikri and
Sennrich, Rico and
Hooker, Sara",
booktitle = "Proceedings of the First Workshop on Multilingual Multicultural Evaluation",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mme-main.9/",
pages = "149--161",
ISBN = "979-8-89176-368-5",
abstract = "Argument mining in multilingual settings has rarely been investigated, due to the lack of annotated resources and to the inherent difficulty of the task. We benchmark the performance of models on cross-lingual and cross-country argument component detection, focusing on political data from the US and France. To do so, we introduce FrenchPolArg, a corpus of argumentative political discourse in French, and we automatically translate already existing US-English resources. We benchmark three different cross-lingual and cross-country pipelines, and compare their results to find the best-performing one. We obtain promising results to be integrated in semi-automatic annotation workflows to reduce the time and cost of annotations."
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<abstract>Argument mining in multilingual settings has rarely been investigated, due to the lack of annotated resources and to the inherent difficulty of the task. We benchmark the performance of models on cross-lingual and cross-country argument component detection, focusing on political data from the US and France. To do so, we introduce FrenchPolArg, a corpus of argumentative political discourse in French, and we automatically translate already existing US-English resources. We benchmark three different cross-lingual and cross-country pipelines, and compare their results to find the best-performing one. We obtain promising results to be integrated in semi-automatic annotation workflows to reduce the time and cost of annotations.</abstract>
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%0 Conference Proceedings
%T Cross-lingual and cross-country approaches to argument component detection: a comparative study.
%A Graiff, Cecilia
%A Clavel, Chloé
%A Sagot, Benoît
%Y Chen, Pinzhen
%Y Zouhar, Vilém
%Y Hu, Hanxu
%Y Khanuja, Simran
%Y Zhu, Wenhao
%Y Haddow, Barry
%Y Birch, Alexandra
%Y Aji, Alham Fikri
%Y Sennrich, Rico
%Y Hooker, Sara
%S Proceedings of the First Workshop on Multilingual Multicultural Evaluation
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-368-5
%F graiff-etal-2026-cross
%X Argument mining in multilingual settings has rarely been investigated, due to the lack of annotated resources and to the inherent difficulty of the task. We benchmark the performance of models on cross-lingual and cross-country argument component detection, focusing on political data from the US and France. To do so, we introduce FrenchPolArg, a corpus of argumentative political discourse in French, and we automatically translate already existing US-English resources. We benchmark three different cross-lingual and cross-country pipelines, and compare their results to find the best-performing one. We obtain promising results to be integrated in semi-automatic annotation workflows to reduce the time and cost of annotations.
%U https://aclanthology.org/2026.mme-main.9/
%P 149-161
Markdown (Informal)
[Cross-lingual and cross-country approaches to argument component detection: a comparative study.](https://aclanthology.org/2026.mme-main.9/) (Graiff et al., MME 2026)
ACL