Machine Translation of Covid-19 Information Resources via Multilingual Transfer

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440554" target="_blank" >RIV/00216208:11320/21:10440554 - isvavai.cz</a>

Výsledek na webu

<a href="https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper26.pdf" target="_blank" >https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper26.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Machine Translation of Covid-19 Information Resources via Multilingual Transfer

Popis výsledku v původním jazyce

The Covid-19 pandemic has created a global demand for accurate and up-to-date information which often originates in English and needs to be translated. To train a machine translation system for such a narrow topic, we leverage in-domain training data in other languages both from related and unrelated language families. We experiment with different transfer learning schedules and observe that transferring via more than one auxiliary language brings the most improvement. We compare the performance with joint multilingual training and report superior results of the transfer learning approach.

Název v anglickém jazyce

Machine Translation of Covid-19 Information Resources via Multilingual Transfer

Popis výsledku anglicky

The Covid-19 pandemic has created a global demand for accurate and up-to-date information which often originates in English and needs to be translated. To train a machine translation system for such a narrow topic, we leverage in-domain training data in other languages both from related and unrelated language families. We experiment with different transfer learning schedules and observe that transferring via more than one auxiliary language brings the most improvement. We compare the performance with joint multilingual training and report superior results of the transfer learning approach.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/GX19-26934X" target="_blank" >GX19-26934X: Neuronové reprezentace v multimodálním a mnohojazyčném modelování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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