@inproceedings{saadat-yazdi-etal-2023-uncovering,
    title = "Uncovering Implicit Inferences for Improved Relational Argument Mining",
    author = "Saadat-Yazdi, Ameer  and
      Pan, Jeff Z.  and
      Kokciyan, Nadin",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2023.eacl-main.182/",
    doi = "10.18653/v1/2023.eacl-main.182",
    pages = "2484--2495",
    abstract = "Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations between arguments (such as attack, support, and neutral) is a challenging task because two arguments may be related to each other via implicit inferences. This task often requires external commonsense knowledge to discover how one argument relates to another. State-of-the-art methods, however, rely on pre-defined knowledge graphs, and thus might not cover target argument pairs well. We introduce a new generative neuro-symbolic approach to finding inference chains that connect the argument pairs by making use of the Commonsense Transformer (COMET). We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks. Our approach significantly outperforms the state-of-the-art, by 2-5{\%} in F1 score, on all three datasets."
}