@inproceedings{Hong2018,
title = {Learning Distributed Event Representations with a Multi-Task Approach},
author = {Xudong Hong and Asad Sayeed and Vera Demberg},
url = {https://aclanthology.org/S18-2002},
doi = {https://doi.org/10.18653/v1/S18-2002},
year = {2018},
date = {2018},
booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
pages = {11-21},
publisher = {Association for Computational Linguistics},
address = {New Orleans, USA},
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.},
pubstate = {published},
type = {inproceedings}
}