Please use this identifier to cite or link to this item: doi:10.22028/D291-30970
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Title: Learning distributed event representations with a multi-task approach
Author(s): Hong, Xudong
Sayeed, Asad
Demberg, Vera
Editor(s): Nissim, Malvina
Berant, Jonathan
Lenci, Alessandro
Language: English
Title: Lexical and Computational Semantics (*SEM 2018) - proceedings of the 7th conference : June 5-6, 2018, New Orleans : NAACL HLT 2018
Startpage: 11
Endpage: 21
Publisher/Platform: ACL
Year of Publication: 2018
Place of publication: Stroudsburg, PA
Title of the Conference: NAACL-HLT 2018
Place of the conference: New Orleans, Louisiana, USA
Publikation type: Conference Paper
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.
DOI of the first publication: 10.18653/v1/S18-2002
URL of the first publication:
Link to this record: hdl:20.500.11880/29720
ISBN: 978-1-948087-22-3
Date of registration: 23-Sep-2020
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Vera Demberg
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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