Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

Miguel Angel Rios Gaona; Raluca-Maria Chereji; Alina Secara; Dragos Ioan Ciobanu

Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

Institut für Translationswissenschaft

Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed

Abstract

Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.

Originalsprache	Englisch
Titel	Translating and the Computer 44 Proceedings
Redakteure*innen	Joss Moorkens, Vilelmini Sosoni
Erscheinungsort	Geneva
Verlag	Editions Tradulex
Seiten	27-38
ISBN (elektronisch)	978-2-9701733-0-4
Publikationsstatus	Veröffentlicht - Sept. 2023
Veranstaltung	Translating and the Computer - European Convention Center Luxembourg (ECCL), Luxembourg, Luxemburg Dauer: 24 Nov. 2022 → 25 Nov. 2022 Konferenznummer: 44 https://asling.org/tc44/

Konferenz

Konferenz	Translating and the Computer
Kurztitel	TC44
Land/Gebiet	Luxemburg
Ort	Luxembourg
Zeitraum	24/11/22 → 25/11/22
Internetadresse	https://asling.org/tc44/

ÖFOS 2012

602051 Translationswissenschaft

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https://www.tradulex.com/varia/TC44-luxembourg2022.pdfLizenz: Nicht angegeben

Zitationsweisen

@inproceedings{d0a4fffd3dab45049ab72c2a937457c9,

title = "Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain",

abstract = "Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.",

keywords = "Neural Machine Translation, domain adaptation, automatic metrics, error annotation, medical domain, English-Romanian",

author = "\{Rios Gaona\}, \{Miguel Angel\} and Raluca-Maria Chereji and Alina Secara and Ciobanu, \{Dragos Ioan\}",

year = "2023",

month = sep,

language = "English",

pages = "27--38",

editor = "Joss Moorkens and Vilelmini Sosoni",

booktitle = "Translating and the Computer 44 Proceedings",

publisher = "Editions Tradulex",

note = "Translating and the Computer, TC44 ; Conference date: 24-11-2022 Through 25-11-2022",

url = "https://asling.org/tc44/",

}

Rios Gaona, MA, Chereji, R-M, Secara, A & Ciobanu, DI 2023, Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain. in J Moorkens & V Sosoni (Hrsg.), Translating and the Computer 44 Proceedings. Editions Tradulex, Geneva, S. 27-38, Translating and the Computer, Luxembourg, Luxemburg, 24/11/22. <https://www.tradulex.com/varia/TC44-luxembourg2022.pdf>

Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain. / Rios Gaona, Miguel Angel; Chereji, Raluca-Maria; Secara, Alina et al.
Translating and the Computer 44 Proceedings. Hrsg. / Joss Moorkens; Vilelmini Sosoni. Geneva: Editions Tradulex, 2023. S. 27-38.

Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed

TY - GEN

T1 - Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

AU - Rios Gaona, Miguel Angel

AU - Chereji, Raluca-Maria

AU - Secara, Alina

AU - Ciobanu, Dragos Ioan

N1 - Conference code: 44

PY - 2023/9

Y1 - 2023/9

N2 - Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.

AB - Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.

KW - Neural Machine Translation

KW - domain adaptation

KW - automatic metrics

KW - error annotation

KW - medical domain

KW - English-Romanian

M3 - Contribution to proceedings

SP - 27

EP - 38

BT - Translating and the Computer 44 Proceedings

A2 - Moorkens, Joss

A2 - Sosoni, Vilelmini

PB - Editions Tradulex

CY - Geneva

T2 - Translating and the Computer

Y2 - 24 November 2022 through 25 November 2022

ER -

Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

Abstract

Konferenz

ÖFOS 2012

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Zitationsweisen