@inproceedings{knaebel-etal-2019-window,
    title = "Window-Based Neural Tagging for Shallow Discourse Argument Labeling",
    author = "Knaebel, Ren{\'e}  and
      Stede, Manfred  and
      Stober, Sebastian",
    editor = "Bansal, Mohit  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/K19-1072/",
    doi = "10.18653/v1/K19-1072",
    pages = "768--777",
    abstract = "This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure. For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model. The model is trained and evaluated on the Penn Discourse Treebank 2 corpus. While it is not as good as knowledge-intense approaches, it clearly outperforms other models that are also trained without additional linguistic features."
}