@inproceedings{liu-etal-2020-metaphor,
title = "Metaphor Detection Using Contextual Word Embeddings From Transformers",
author = "Liu, Jerry and
O{'}Hara, Nathan and
Rubin, Alexander and
Draelos, Rachel and
Rudin, Cynthia",
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-1.34",
doi = "10.18653/v1/2020.figlang-1.34",
pages = "250--255",
abstract = "The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0{\%} on VUA AllPOS, 73.0{\%} on VUA Verbs, 66.9{\%} on TOEFL AllPOS, and 69.7{\%} on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.",
}
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<abstract>The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0% on VUA AllPOS, 73.0% on VUA Verbs, 66.9% on TOEFL AllPOS, and 69.7% on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.</abstract>
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%0 Conference Proceedings
%T Metaphor Detection Using Contextual Word Embeddings From Transformers
%A Liu, Jerry
%A O’Hara, Nathan
%A Rubin, Alexander
%A Draelos, Rachel
%A Rudin, Cynthia
%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 liu-etal-2020-metaphor
%X The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0% on VUA AllPOS, 73.0% on VUA Verbs, 66.9% on TOEFL AllPOS, and 69.7% on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.
%R 10.18653/v1/2020.figlang-1.34
%U https://aclanthology.org/2020.figlang-1.34
%U https://doi.org/10.18653/v1/2020.figlang-1.34
%P 250-255
Markdown (Informal)
[Metaphor Detection Using Contextual Word Embeddings From Transformers](https://aclanthology.org/2020.figlang-1.34) (Liu et al., Fig-Lang 2020)
ACL