ParaMed: a parallel corpus for English-Chinese translation in the biomedical domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10439972" target="_blank" >RIV/00216208:11320/21:10439972 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=bYTSpH52Ct" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=bYTSpH52Ct</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12911-021-01621-8" target="_blank" >10.1186/s12911-021-01621-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ParaMed: a parallel corpus for English-Chinese translation in the biomedical domain

Popis výsledku v původním jazyce

Background: Biomedical language translation requires multi-lingual fluency as well as relevant domain knowledge. Such requirements make it challenging to train qualified translators and costly to generate high-quality translations. Machine translation represents an effective alternative, but accurate machine translation requires large amounts of in-domain data. While such datasets are abundant in general domains, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge, a parallel corpus does not exist for this language pair in the biomedical domain. Description: We developed an effective pipeline to acquire and process an English-Chinese parallel corpus from the New England Journal of Medicine (NEJM). This corpus consists of about 100,000 sentence pairs and 3,000,000 tokens on each side. We showed that training on out-of-domain data and fine-tuning with as few as 4000 NEJM sentence pairs improve translation quality by 25.3 (13.4) BLEU for enRIGHTWARDS ARROW zh (zhRIGHTWARDS ARROW en) directions. Translation quality continues to improve at a slower pace on larger in-domain data subsets, with a total increase of 33.0 (24.3) BLEU for enRIGHTWARDS ARROW zh (zhRIGHTWARDS ARROW en) directions on the full dataset. Conclusions: The code and data are available at https://github.com/boxiangliu/ParaMed.

Název v anglickém jazyce

ParaMed: a parallel corpus for English-Chinese translation in the biomedical domain

Popis výsledku anglicky

Background: Biomedical language translation requires multi-lingual fluency as well as relevant domain knowledge. Such requirements make it challenging to train qualified translators and costly to generate high-quality translations. Machine translation represents an effective alternative, but accurate machine translation requires large amounts of in-domain data. While such datasets are abundant in general domains, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge, a parallel corpus does not exist for this language pair in the biomedical domain. Description: We developed an effective pipeline to acquire and process an English-Chinese parallel corpus from the New England Journal of Medicine (NEJM). This corpus consists of about 100,000 sentence pairs and 3,000,000 tokens on each side. We showed that training on out-of-domain data and fine-tuning with as few as 4000 NEJM sentence pairs improve translation quality by 25.3 (13.4) BLEU for enRIGHTWARDS ARROW zh (zhRIGHTWARDS ARROW en) directions. Translation quality continues to improve at a slower pace on larger in-domain data subsets, with a total increase of 33.0 (24.3) BLEU for enRIGHTWARDS ARROW zh (zhRIGHTWARDS ARROW en) directions on the full dataset. Conclusions: The code and data are available at https://github.com/boxiangliu/ParaMed.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

BMC Medical Informatics and Decision Making

ISSN

1472-6947

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

258

Kód UT WoS článku

000693245800001

EID výsledku v databázi Scopus

2-s2.0-85114307208