%0 Conference Proceedings %T Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations %A Lee, I-Ta %A Pacheco, Maria Leonor %A Goldwasser, Dan %Y Cohn, Trevor %Y He, Yulan %Y Liu, Yang %S Findings of the Association for Computational Linguistics: EMNLP 2020 %D 2020 %8 November %I Association for Computational Linguistics %C Online %F lee-etal-2020-weakly %X 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. %R 10.18653/v1/2020.findings-emnlp.446 %U https://aclanthology.org/2020.findings-emnlp.446 %U https://doi.org/10.18653/v1/2020.findings-emnlp.446 %P 4962-4972