【摘要】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.
【作者單位】Dept. of Language Science and Technology, Saarland University; Dept. of Philosophy, Linguistics, and Theory of Science, University of Gothenburg; Dept. of Language Science and Technology, Saarland University;