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Multilingual Clinical NER: Translation or Cross-lingual Transfer?

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ASR9KPR9Y" target="_blank" >RIV/00216208:11320/23:SR9KPR9Y - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175474593&partnerID=40&md5=676e67f8858c45aab9d9d558d1dec0bf" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175474593&partnerID=40&md5=676e67f8858c45aab9d9d558d1dec0bf</a>

  • DOI - Digital Object Identifier

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Multilingual Clinical NER: Translation or Cross-lingual Transfer?

  • Popis výsledku v původním jazyce

    "Natural language tasks like Named Entity Recognition (NER) in the clinical domain on non-English texts can be very time-consuming and expensive due to the lack of annotated data. Cross-lingual transfer (CLT) is a way to circumvent this issue thanks to the ability of multilingual large language models to be fine-tuned on a specific task in one language and to provide high accuracy for the same task in another language. However, other methods leveraging translation models can be used to perform NER without annotated data in the target language, by either translating the training set or test set. This paper compares cross-lingual transfer with these two alternative methods, to perform clinical NER in French and in German without any training data in those languages. To this end, we release MedNERF a medical NER test set extracted from French drug prescriptions and annotated with the same guidelines as an English dataset. Through extensive experiments on this dataset and on a German medical dataset (Frei and Kramer, 2021), we show that translation-based methods can achieve similar performance to CLT but require more care in their design. And while they can take advantage of monolingual clinical language models, those do not guarantee better results than large general-purpose multilingual models, whether with cross-lingual transfer or translation. © 2023 Association for Computational Linguistics."

  • Název v anglickém jazyce

    Multilingual Clinical NER: Translation or Cross-lingual Transfer?

  • Popis výsledku anglicky

    "Natural language tasks like Named Entity Recognition (NER) in the clinical domain on non-English texts can be very time-consuming and expensive due to the lack of annotated data. Cross-lingual transfer (CLT) is a way to circumvent this issue thanks to the ability of multilingual large language models to be fine-tuned on a specific task in one language and to provide high accuracy for the same task in another language. However, other methods leveraging translation models can be used to perform NER without annotated data in the target language, by either translating the training set or test set. This paper compares cross-lingual transfer with these two alternative methods, to perform clinical NER in French and in German without any training data in those languages. To this end, we release MedNERF a medical NER test set extracted from French drug prescriptions and annotated with the same guidelines as an English dataset. Through extensive experiments on this dataset and on a German medical dataset (Frei and Kramer, 2021), we show that translation-based methods can achieve similar performance to CLT but require more care in their design. And while they can take advantage of monolingual clinical language models, those do not guarantee better results than large general-purpose multilingual models, whether with cross-lingual transfer or translation. © 2023 Association for Computational Linguistics."

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Návaznosti výsledku

  • Projekt

  • Návaznosti

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    "Proc. Annu. Meet. Assoc. Comput Linguist."

  • ISBN

    978-195942988-3

  • ISSN

    0736-587X

  • e-ISSN

  • Počet stran výsledku

    23

  • Strana od-do

    289-311

  • Název nakladatele

    Association for Computational Linguistics (ACL)

  • Místo vydání

  • Místo konání akce

    Melaka, Malaysia

  • Datum konání akce

    1. 1. 2023

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku