<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="epure-etal-2020-modeling">
    <titleInfo>
        <title>Modeling the Music Genre Perception across Language-Bound Cultures</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="given">V</namePart>
        <namePart type="family">Epure</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Guillaume</namePart>
        <namePart type="family">Salha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Manuel</namePart>
        <namePart type="family">Moussallam</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Romain</namePart>
        <namePart type="family">Hennequin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</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 music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.</abstract>
    <identifier type="citekey">epure-etal-2020-modeling</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-main.386</identifier>
    <location>
        <url>https://aclanthology.org/2020.emnlp-main.386</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>4765</start>
            <end>4779</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Modeling the Music Genre Perception across Language-Bound Cultures
%A Epure, Elena V.
%A Salha, Guillaume
%A Moussallam, Manuel
%A Hennequin, Romain
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F epure-etal-2020-modeling
%X The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.
%R 10.18653/v1/2020.emnlp-main.386
%U https://aclanthology.org/2020.emnlp-main.386
%U https://doi.org/10.18653/v1/2020.emnlp-main.386
%P 4765-4779

Markdown (Informal)

[Modeling the Music Genre Perception across Language-Bound Cultures](https://aclanthology.org/2020.emnlp-main.386) (Epure et al., EMNLP 2020)

• Modeling the Music Genre Perception across Language-Bound Cultures (Epure et al., EMNLP 2020)

ACL

• Elena V. Epure, Guillaume Salha, Manuel Moussallam, and Romain Hennequin. 2020. Modeling the Music Genre Perception across Language-Bound Cultures. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4765–4779, Online. Association for Computational Linguistics.