Learning distributed event representations with a multi-task approach

Xudong Hong, Asad Sayeed, Vera Demberg


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.
Anthology ID:
S18-2002
Volume:
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Malvina Nissim, Jonathan Berant, Alessandro Lenci
Venue:
*SEM
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–21
Language:
URL:
https://aclanthology.org/S18-2002
DOI:
10.18653/v1/S18-2002
Bibkey:
Cite (ACL):
Xudong Hong, Asad Sayeed, and Vera Demberg. 2018. Learning distributed event representations with a multi-task approach. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 11–21, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Learning distributed event representations with a multi-task approach (Hong et al., *SEM 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-2002.pdf