COVID-19和其他新兴领域的动态术语集成
Dynamic Terminology Integration for COVID-19 and other Emerging Domains
摘要：
大多数语言领域都需要谨慎使用术语，以确保所传达信息的清晰度和充分性。虽然可以通过在大量域内并行数据上调整通用MT系统来实现某些语言和领域术语的正确使用，但对于资源较少的语言和利基领域，很少有这种特定领域的数据。此外，正如最近COVID-19所示，新兴领域没有特定领域的并行数据。然而，最近这场灾难的严重性造成了对有关预防流行病和感染的重要信息的可靠翻译的高度需求。这项工作是WMT 2021共享任务的一部分：使用术语的机器翻译，我们描述了能够在翻译时动态集成术语的Tilde MT系统。我们的系统在EN-FR语言对的测试集上实现了高达94%的COVID-19术语使用准确率，而无需在系统训练期间访问任何形式的域内信息。最后，我们对共享任务本身和机器翻译中的术语翻译进行了更广泛的讨论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
