@inproceedings{alnafesah-etal-2020-augmenting,
title = "Augmenting Neural Metaphor Detection with Concreteness",
author = "Alnafesah, Ghadi and
Tayyar Madabushi, Harish and
Lee, Mark",
editor = "Klebanov, Beata Beigman and
Shutova, Ekaterina and
Lichtenstein, Patricia and
Muresan, Smaranda and
Wee, Chee and
Feldman, Anna and
Ghosh, Debanjan",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.figlang-1.28",
doi = "10.18653/v1/2020.figlang-1.28",
pages = "204--210",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Augmenting Neural Metaphor Detection with Concreteness
%A Alnafesah, Ghadi
%A Tayyar Madabushi, Harish
%A Lee, Mark
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F alnafesah-etal-2020-augmenting
%X 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.
%R 10.18653/v1/2020.figlang-1.28
%U https://aclanthology.org/2020.figlang-1.28
%U https://doi.org/10.18653/v1/2020.figlang-1.28
%P 204-210
Markdown (Informal)
[Augmenting Neural Metaphor Detection with Concreteness](https://aclanthology.org/2020.figlang-1.28) (Alnafesah et al., Fig-Lang 2020)
ACL