@InProceedings{galassi-lippi-torroni:2018:W18-52,
  author    = {Galassi, Andrea  and  Lippi, Marco  and  Torroni, Paolo},
  title     = {Argumentative Link Prediction using Residual Networks and Multi-Objective Learning},
  booktitle = {Proceedings of the 5th Workshop on Argument Mining},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {1--10},
  abstract  = {We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method 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.},
  url       = {http://www.aclweb.org/anthology/W18-5201}
}