@InProceedings{hong-sayeed-demberg:2018:S18-2,
  author    = {Hong, Xudong  and  Sayeed, Asad  and  Demberg, Vera},
  title     = {Learning distributed event representations with a multi-task approach},
  booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {11--21},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/S18-2002}
}