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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.446</identifier>
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%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
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 nov
%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

Markdown (Informal)

[Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations](https://aclanthology.org/2020.findings-emnlp.446) (Lee et al., Findings 2020)

• Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations (Lee et al., Findings 2020)

ACL

• I-Ta Lee, Maria Leonor Pacheco, and Dan Goldwasser. 2020. Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4962–4972, Online. Association for Computational Linguistics.