Learning distributed event representations with a multi-task approach
Xudong Hong, Asad Sayeed, Vera Demberg · 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 stateof-the-art.