Hong, Xudong; Sayeed, Asad; Demberg, Vera
Learning Distributed Event Representations with a Multi-Task Approach
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, Association for Computational Linguistics, pp. 11-21, New Orleans, USA, 2018.
Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.