@InProceedings{hooda-kosseim:2017:RANLP,
  author    = {Hooda, Sohail  and  Kosseim, Leila},
  title     = {Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {309--315},
  abstract  = {Argument labeling of explicit discourse relations is a challenging task. The
	state of the art systems achieve slightly above 55% F-measure but require
	hand-crafted features. In this paper, we propose a Long Short Term Memory
	(LSTM) based model for argument labeling. We experimented with multiple
	configurations of our model. Using the PDTB dataset, our best model achieved an
	F1 measure of 23.05% without any feature engineering. This is significantly
	higher than the 20.52% achieved by the state of the art RNN approach, but
	significantly lower than the feature based state of the art systems. On the
	other hand, because our approach learns only from the raw dataset, it is more
	widely applicable to multiple textual genres and languages.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_042}
}