@inproceedings{contalbo-etal-2024-argument,
    title = "Argument Relation Classification through Discourse Markers and Adversarial Training",
    author = "Contalbo, Michele Luca  and
      Guerra, Francesco  and
      Paganelli, Matteo",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.1054/",
    doi = "10.18653/v1/2024.emnlp-main.1054",
    pages = "18949--18954",
    abstract = "Argument relation classification (ARC) identifies supportive, contrasting and neutral relations between argumentative units. The current approaches rely on transformer architectures which have proven to be more effective than traditional methods based on hand-crafted linguistic features. In this paper, we introduce DISARM, which advances the state of the art with a training procedure combining multi-task and adversarial learning strategies. By jointly solving the ARC and discourse marker detection tasks and aligning their embedding spaces into a unified latent space, DISARM outperforms the accuracy of existing approaches."
}