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Multidirectional associative optimization of function-specific word representations

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Gerz, D 
Vulić, I 
Rei, M 
Reichart, R 
Korhonen, A 

Abstract

We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.

Description

Keywords

Journal Title

Proceedings of the Annual Meeting of the Association for Computational Linguistics

Conference Name

ACL 2020: 58th Annual Meeting of the Association for Computational Linguistics

Journal ISSN

0736-587X

Volume Title

Publisher

Association for Computational Linguistics

Rights

All rights reserved
Sponsorship
European Research Council (648909)