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.- Anthology ID:
- 2020.lrec-1.726
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5928–5933
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.726
- DOI:
- Bibkey:
- Cite (ACL):
- Gitit Kehat and James Pustejovsky. 2020. Improving Neural Metaphor Detection with Visual Datasets. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5928–5933, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Improving Neural Metaphor Detection with Visual Datasets (Kehat & Pustejovsky, LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.726.pdf
Export citation
@inproceedings{kehat-pustejovsky-2020-improving,
title = "Improving Neural Metaphor Detection with Visual Datasets",
author = "Kehat, Gitit and
Pustejovsky, James",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth 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 %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth 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)
- Improving Neural Metaphor Detection with Visual Datasets (Kehat & Pustejovsky, LREC 2020)
ACL
- Gitit Kehat and James Pustejovsky. 2020. Improving Neural Metaphor Detection with Visual Datasets. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5928–5933, Marseille, France. European Language Resources Association.