@InProceedings{potash-romanov-rumshisky:2017:EMNLP2017,
  author    = {Potash, Peter  and  Romanov, Alexey  and  Rumshisky, Anna},
  title     = {Here's My Point: Joint Pointer Architecture for Argument Mining},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1364--1373},
  abstract  = {In order to determine argument structure in text, one must understand how
	individual components of the overall argument are linked. This work presents
	the first neural network-based approach to link extraction in argument mining.
	Specifically, we propose a novel architecture that applies Pointer Network
	sequence-to-sequence attention modeling to structural prediction in discourse
	parsing tasks. We then develop a joint model that extends this architecture to
	simultaneously address the link extraction task and the classification of
	argument components. The proposed joint model achieves state-of-the-art results
	on two separate evaluation corpora, showing far superior performance than the
	previously proposed corpus-specific and heavily feature-engineered models.
	Furthermore, our results demonstrate that jointly optimizing for both tasks is
	crucial for high performance.},
  url       = {https://www.aclweb.org/anthology/D17-1143}
}