@inproceedings{ruiz-dolz-etal-2024-learning,
    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.",
}

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.
Ruiz-Dolz, Ramon Chiu, Chr-Jr Chen, Chung-Chi K o, Noriko Chen, Hsin-Hsi
Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain