@inproceedings{schneider-etal-2022-metaphor,
title = "Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in {M}iddle {H}igh {G}erman",
author = "Schneider, Felix and
Sickert, Sven and
Brandes, Phillip and
Marshall, Sophie and
Denzler, Joachim",
editor = "Bhatia, Archna and
Cook, Paul and
Taslimipoor, Shiva and
Garcia, Marcos and
Ramisch, Carlos",
booktitle = "Proceedings of the 18th Workshop on Multiword Expressions @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.mwe-1.11",
pages = "75--80",
abstract = "In this work, we present a novel unsupervised method for adjective-noun metaphor detection on low resource languages. We propose two new approaches: First, a way of artificially generating metaphor training examples and second, a novel way to find metaphors relying only on word embeddings. The latter enables application for low resource languages. Our method is based on a transformation of word embedding vectors into another vector space, in which the distance between the adjective word vector and the noun word vector represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating artificial metaphor examples and show that our approach can be used to generate a metaphor dataset with low annotation cost. It can then be used to finetune the system in a few-shot manner. In our experiments we show the capabilities of the method in its unsupervised and in its supervised version. Additionally, we test it against a comparable unsupervised baseline method and a supervised variation of it.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="schneider-etal-2022-metaphor">
<titleInfo>
<title>Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German</title>
</titleInfo>
<name type="personal">
<namePart type="given">Felix</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sven</namePart>
<namePart type="family">Sickert</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Phillip</namePart>
<namePart type="family">Brandes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sophie</namePart>
<namePart type="family">Marshall</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joachim</namePart>
<namePart type="family">Denzler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th Workshop on Multiword Expressions @LREC2022</title>
</titleInfo>
<name type="personal">
<namePart type="given">Archna</namePart>
<namePart type="family">Bhatia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paul</namePart>
<namePart type="family">Cook</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shiva</namePart>
<namePart type="family">Taslimipoor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcos</namePart>
<namePart type="family">Garcia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carlos</namePart>
<namePart type="family">Ramisch</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Language Resources Association</publisher>
<place>
<placeTerm type="text">Marseille, France</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this work, we present a novel unsupervised method for adjective-noun metaphor detection on low resource languages. We propose two new approaches: First, a way of artificially generating metaphor training examples and second, a novel way to find metaphors relying only on word embeddings. The latter enables application for low resource languages. Our method is based on a transformation of word embedding vectors into another vector space, in which the distance between the adjective word vector and the noun word vector represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating artificial metaphor examples and show that our approach can be used to generate a metaphor dataset with low annotation cost. It can then be used to finetune the system in a few-shot manner. In our experiments we show the capabilities of the method in its unsupervised and in its supervised version. Additionally, we test it against a comparable unsupervised baseline method and a supervised variation of it.</abstract>
<identifier type="citekey">schneider-etal-2022-metaphor</identifier>
<location>
<url>https://aclanthology.org/2022.mwe-1.11</url>
</location>
<part>
<date>2022-06</date>
<extent unit="page">
<start>75</start>
<end>80</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German
%A Schneider, Felix
%A Sickert, Sven
%A Brandes, Phillip
%A Marshall, Sophie
%A Denzler, Joachim
%Y Bhatia, Archna
%Y Cook, Paul
%Y Taslimipoor, Shiva
%Y Garcia, Marcos
%Y Ramisch, Carlos
%S Proceedings of the 18th Workshop on Multiword Expressions @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F schneider-etal-2022-metaphor
%X In this work, we present a novel unsupervised method for adjective-noun metaphor detection on low resource languages. We propose two new approaches: First, a way of artificially generating metaphor training examples and second, a novel way to find metaphors relying only on word embeddings. The latter enables application for low resource languages. Our method is based on a transformation of word embedding vectors into another vector space, in which the distance between the adjective word vector and the noun word vector represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating artificial metaphor examples and show that our approach can be used to generate a metaphor dataset with low annotation cost. It can then be used to finetune the system in a few-shot manner. In our experiments we show the capabilities of the method in its unsupervised and in its supervised version. Additionally, we test it against a comparable unsupervised baseline method and a supervised variation of it.
%U https://aclanthology.org/2022.mwe-1.11
%P 75-80
Markdown (Informal)
[Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German](https://aclanthology.org/2022.mwe-1.11) (Schneider et al., MWE 2022)
ACL