@inproceedings{lee-etal-2020-weakly,
    title = "Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations",
    author = "Lee, I-Ta  and
      Pacheco, Maria Leonor  and
      Goldwasser, Dan",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.findings-emnlp.446/",
    doi = "10.18653/v1/2020.findings-emnlp.446",
    pages = "4962--4972",
    abstract = "Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types. This paper suggests to represent this type of information as a narrative graph and learn contextualized event representations over it using a relational graph neural network model. We train our model to capture event relations, derived from the Penn Discourse Tree Bank, on a huge corpus, and show that our multi-relational contextualized event representation can improve performance when learning script knowledge without direct supervision and provide a better representation for the implicit discourse sense classification task."
}