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 language | English |
|---|---|
| Title of host publication | Wmt 2021 6th Conference on Machine Translation Proceedings |
| Place of Publication | [Stroudsburg |
| Publisher | Association for Computational Linguistics] |
| Pages | 821-827 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781954085947 |
| ISBN (Print) | 9781954085947 |
| Publication status | Published - 2021 |
Publication series
| Name | WMT 2021 - 6th Conference on Machine Translation, Proceedings |
|---|
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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