@inproceedings{kehat-pustejovsky-2020-improving,
title = "Improving Neural Metaphor Detection with Visual Datasets",
author = "Kehat, Gitit and
Pustejovsky, James",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.726",
pages = "5928--5933",
abstract = "We present new results on Metaphor Detection by using text from visual datasets. Using a straightforward technique for sampling text from Vision-Language datasets, we create a data structure we term a visibility word embedding. We then combine these embeddings in a relatively simple BiLSTM module augmented with contextualized word representations (ELMo), and show improvement over previous state-of-the-art approaches that use more complex neural network architectures and richer linguistic features, for the task of verb classification.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>We present new results on Metaphor Detection by using text from visual datasets. Using a straightforward technique for sampling text from Vision-Language datasets, we create a data structure we term a visibility word embedding. We then combine these embeddings in a relatively simple BiLSTM module augmented with contextualized word representations (ELMo), and show improvement over previous state-of-the-art approaches that use more complex neural network architectures and richer linguistic features, for the task of verb classification.</abstract>
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%0 Conference Proceedings
%T Improving Neural Metaphor Detection with Visual Datasets
%A Kehat, Gitit
%A Pustejovsky, James
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F kehat-pustejovsky-2020-improving
%X We present new results on Metaphor Detection by using text from visual datasets. Using a straightforward technique for sampling text from Vision-Language datasets, we create a data structure we term a visibility word embedding. We then combine these embeddings in a relatively simple BiLSTM module augmented with contextualized word representations (ELMo), and show improvement over previous state-of-the-art approaches that use more complex neural network architectures and richer linguistic features, for the task of verb classification.
%U https://aclanthology.org/2020.lrec-1.726
%P 5928-5933
Markdown (Informal)
[Improving Neural Metaphor Detection with Visual Datasets](https://aclanthology.org/2020.lrec-1.726) (Kehat & Pustejovsky, LREC 2020)
ACL