Augmenting Neural Metaphor Detection with Concreteness

Ghadi Alnafesah, Harish Tayyar Madabushi, Mark Lee

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

Abstract

The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored concreteness and related psycholinguistic information. We hypothesis that this information is not available to these models and that their addition will boost the performance of these models in detecting metaphor. We test this hypothesis on the Metaphor Detection Shared Task 2020 and find that the addition of concreteness information does in fact boost deep neural models. We also run tests on data from a previous shared task and show similar results.
Original languageEnglish
Title of host publicationProceedings of the Second Workshop on Figurative Language Processing
Place of PublicationOnline
PublisherAssociation for Computational Linguistics
Pages204-210
Number of pages7
DOIs
Publication statusPublished - 1 Jul 2020

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