<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liu-etal-2020-metaphor">
    <titleInfo>
        <title>Metaphor Detection Using Contextual Word Embeddings From Transformers</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jerry</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nathan</namePart>
        <namePart type="family">O’Hara</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Rubin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rachel</namePart>
        <namePart type="family">Draelos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Cynthia</namePart>
        <namePart type="family">Rudin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Second Workshop on Figurative Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Beata</namePart>
            <namePart type="given">Beigman</namePart>
            <namePart type="family">Klebanov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Patricia</namePart>
            <namePart type="family">Lichtenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chee</namePart>
            <namePart type="family">Wee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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>
    <identifier type="citekey">liu-etal-2020-metaphor</identifier>
    <identifier type="doi">10.18653/v1/2020.figlang-1.34</identifier>
    <location>
        <url>https://aclanthology.org/2020.figlang-1.34</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>250</start>
            <end>255</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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)

• Metaphor Detection Using Contextual Word Embeddings From Transformers (Liu et al., Fig-Lang 2020)

ACL

• Jerry Liu, Nathan O’Hara, Alexander Rubin, Rachel Draelos, and Cynthia Rudin. 2020. Metaphor Detection Using Contextual Word Embeddings From Transformers. In Proceedings of the Second Workshop on Figurative Language Processing, pages 250–255, Online. Association for Computational Linguistics.