Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations
I-Ta Lee, María Leonor Pacheco, Dan L. Goldwasser · 2020
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.