@inproceedings{galassi-etal-2018-argumentative,
    title = "Argumentative Link Prediction using Residual Networks and Multi-Objective Learning",
    author = "Galassi, Andrea  and
      Lippi, Marco  and
      Torroni, Paolo",
    editor = "Slonim, Noam  and
      Aharonov, Ranit",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-5201",
    doi = "10.18653/v1/W18-5201",
    pages = "1--10",
    abstract = "We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.",
}