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MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations

2024Comunicação em conferência Acesso aberto
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dc.contributor.authorGromann, Dagmar
dc.contributor.authorOliveira, Hugo Gonçalo
dc.contributor.authorPitarch, Lucia
dc.contributor.authorApostol, Elena-Simona
dc.contributor.authorBernad, Jordi
dc.contributor.authorBytyçi, Eliot
dc.contributor.authorCantone, Chiara
dc.contributor.authorCarvalho, Sara
dc.contributor.authorFrontini, Francesca
dc.contributor.authorGarabik, Radovan
dc.contributor.authorGracia, Jorge
dc.contributor.authorGranata, Letzia
dc.contributor.authorKhan, Fahad
dc.contributor.authorKnez, Timotej
dc.contributor.authorLabropoulou, Penny
dc.contributor.authorLiebeskind, Chaya
dc.contributor.authorBuono, Maria Pia di
dc.contributor.authorAnić, Ana Ostroški
dc.contributor.authorRackevičienė, Sigita
dc.contributor.authorRodrigues, Ricardo
dc.contributor.authorSérasse, Gilles
dc.contributor.authorSelmistraitis, Linas
dc.contributor.authorSidibé, Mahammadou
dc.contributor.authorSilvano, Purificação
dc.contributor.authorSpahiu, Blerina
dc.contributor.authorSogutlu, Enriketa
dc.contributor.authorStanković, Ranka
dc.contributor.authorTruică, Ciprian-Octavian
dc.contributor.authorOleškevičienė, Giedrė Valūnaitė
dc.contributor.authorZitnik, Slavko
dc.contributor.authorZdravkova, Katerina
dc.date.accessioned2026-06-01T13:43:02Z
dc.date.available2026-06-01T13:43:02Z
dc.date.issued2024
dc.description.abstractUnderstanding the relation between the meanings of words is an important part of comprehending natural language. Prior work has either focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs), with some exceptions. Given the rarity of highly multilingual benchmarks, it is unclear to what extent PLMs capture relational knowledge and are able to transfer it across languages. To start addressing this question, we propose MultiLexBATS, a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages, such as Bambara, Lithuanian, and Albanian. As experiment on cross-lingual transfer of relational knowledge, we test the PLMs’ ability to (1) capture analogies across languages, and (2) predict translation targets. We find considerable differences across relation types and languages with a clear preference for hypernymy and antonymy as well as romance languages.eng
dc.identifier.urihttp://hdl.handle.net/10400.26/63453
dc.language.isoeng
dc.peerreviewedn/a
dc.relationUID/CEC/00326/2020
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectLexical Semantic Relations
dc.subjectMultilingual Benchmark
dc.subjectBATS
dc.titleMultiLexBATS: Multilingual Dataset of Lexical Semantic Relationseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2024
oaire.citation.conferencePlaceTorino
oaire.citation.endPage11793
oaire.citation.startPage11783
oaire.citation.titleProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRodrigues
person.givenNameRicardo
person.identifier.ciencia-idD31C-FB4A-FEAA
person.identifier.orcid0000-0002-6262-7920
relation.isAuthorOfPublicationc64ccf7c-eca2-43cf-a4a2-78e684499c00
relation.isAuthorOfPublication.latestForDiscoveryc64ccf7c-eca2-43cf-a4a2-78e684499c00

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