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Dynamic Terminology Integration for COVID-19 and other Emerging Domains

  • Tilde Company
  • University of Latvia

Research output: Chapter in Book/Report/Conference proceedingConference paperResearchpeer-review

8 Citations (Scopus)

Abstract

The majority of language domains require prudent use of terminology to ensure clarity and adequacy of information conveyed. While the correct use of terminology for some languages and domains can be achieved by adapting general-purpose MT systems on large volumes of in-domain parallel data, such quantities of domain-specific data are seldom available for less-resourced languages and niche domains. Furthermore, as exemplified by COVID-19 recently, no domain-specific parallel data is readily available for emerging domains. However, the gravity of this recent calamity created a high demand for reliable translation of critical information regarding pandemic and infection prevention. This work is part of WMT2021 Shared Task: Machine Translation using Terminologies, where we describe Tilde MT systems that are capable of dynamic terminology integration at the time of translation. Our systems achieve up to 94% COVID-19 term use accuracy on the test set of the EN-FR language pair without having access to any form of in-domain information during system training. We conclude our work with a broader discussion considering the Shared Task itself and terminology translation in MT.

Original languageEnglish
Title of host publicationWmt 2021 6th Conference on Machine Translation Proceedings
Place of Publication[Stroudsburg
PublisherAssociation for Computational Linguistics]
Pages821-827
Number of pages7
ISBN (Electronic)9781954085947
ISBN (Print)9781954085947
Publication statusPublished - 2021

Publication series

NameWMT 2021 - 6th Conference on Machine Translation, Proceedings

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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