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Learning distributed event representations with a multi-task approach

Conference paper
Authors X. Hong
Asad Sayeed
V. Demberg
Published in Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, New Orleans, June 5-6, 2018.
ISBN 978-1-948087-22-3
Publisher Association for Computational Linguistics
Place of publication Stroudsburg, PA, USA
Publication year 2018
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links aclweb.org/anthology/S18-2002
Keywords machine learning, computational linguistics, thematic fit, semantic roles, deep learning, neural networks
Subject categories Computational linguistics

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.

Page Manager: Webmaster|Last update: 9/11/2012
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