@inproceedings{ruiz-dolz-etal-2024-learning-strategies,
title = "Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain",
author = "Ruiz-Dolz, Ramon and
Chiu, Chr-Jr and
Chen, Chung-Chi and
Kando, Noriko and
Chen, Hsin-Hsi",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.898",
pages = "10286--10292",
abstract = "Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when deploying argument mining systems \textit{in the wild}, we might not be able to control some of these features. Therefore, an important aspect that has not been thoroughly investigated in the argument mining literature is the robustness of such systems to variations in language and domain. In this paper, we present a complete analysis across three different languages and three different domains that allow us to have a better understanding on how to leverage the scarce available corpora to design argument mining systems that are more robust to natural language variations.",
}
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<abstract>Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when deploying argument mining systems in the wild, we might not be able to control some of these features. Therefore, an important aspect that has not been thoroughly investigated in the argument mining literature is the robustness of such systems to variations in language and domain. In this paper, we present a complete analysis across three different languages and three different domains that allow us to have a better understanding on how to leverage the scarce available corpora to design argument mining systems that are more robust to natural language variations.</abstract>
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%0 Conference Proceedings
%T Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain
%A Ruiz-Dolz, Ramon
%A Chiu, Chr-Jr
%A Chen, Chung-Chi
%A Kando, Noriko
%A Chen, Hsin-Hsi
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F ruiz-dolz-etal-2024-learning-strategies
%X Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when deploying argument mining systems in the wild, we might not be able to control some of these features. Therefore, an important aspect that has not been thoroughly investigated in the argument mining literature is the robustness of such systems to variations in language and domain. In this paper, we present a complete analysis across three different languages and three different domains that allow us to have a better understanding on how to leverage the scarce available corpora to design argument mining systems that are more robust to natural language variations.
%U https://aclanthology.org/2024.lrec-main.898
%P 10286-10292
Markdown (Informal)
[Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain](https://aclanthology.org/2024.lrec-main.898) (Ruiz-Dolz et al., LREC-COLING 2024)
ACL