Terminology management is subject to automation in the framework of automating translation processes. Over the last 18 months he has familiarised himself with the OpenNMT toolkit and now uses Neural Machine Translation as the basis for the supply of translations to his clients through his company MyDutchPal Ltd. At the age of 50 he taught himself to program and wrote a rule-based Dutch-English machine translation application which has been used to translate documentation for some of the largest engineering projects in Dutch history.įor the past 15 years he has devoted himself to the study and development of translation technology. After some years in South Africa and Brazil, he severed his ties with the Catholic Church and returned to the UK where he worked as a translator, lexicographer (Harrap’s English-Brazilian Portuguese Business Dictionary) and playwright.Īs an external translator for Unesco he translated texts ranging from Mongolian cultural legislation to a book by a minor French existentialist. His religious studies also called for a knowledge of Latin, Greek and Hebrew. Terence Lewis, MITI, entered the world of translation as a young brother in an Italian religious order, when he was entrusted with the task of translating some of the founder’s speeches into English. We also explain how we customize a baseline NMT engine so that it can correctly translate specialist texts without going through the lengthy procedure of training the baseline engine from scratch.īoth these strategies make the output of NMT engines more useful in production settings. The talk will present practical examples of what happens in these routines. Both these modules allow any number of routines to be applied before and after inference (translation) by the NMT engine. This intermediate server provides both pre-processing and post-processing modules. The first involves the implementation of an intermediate server between the client application and the RESTserver (Xavante) that delivers the predictions proposed by the NMT model. We apply two strategies to deal with these issues. Failure to resolve these issues makes NMT output unpalatable to professional translators. texts dealing with a specialised field on which the model has not been trained. Two of the main issues that hamper the implementation of NMT solutions in production settings are the apparent inability to deal with tokens not contained in the model’s vocabulary (‘s, or OOV’s) and the problematic translations generated when the model is applied to translate “out of domain” texts, i.e. The conclusions and discussion will focus on if, when and how we can securely integrate and use online MT in the TM tool based translation process. In addition to highlighting some of the critical sections of the terms of service of popular online MT offerings, I will take a closer look at the needs, technical and organisational options and implications for protecting personal data when using online MT solutions (GDPR compliance). In my talk, I will not cover the typical aspects related to machine translation in the professional translation workflow (post-editing, quality, pricing, process impact, etc.), but rather focus on information security and data protection aspects. Better MT quality and self-learning capabilities thanks to neural and adaptive MT technologies as well as the availability of a large number of MT plugins for TM-based tools make the classic TM and ‘innovative’ MT combination more attractive for translators and LSPs. Information Security and Privacy Aspects of using Online Machine Translation in CAT ToolsĪlmost all translation memory (TM) tools nowadays offer integrations with online machine translation (MT) solutions. We will share the successes and the failures and will conclude by discussing the future actions to be taken in order to keep abreast of the continuously changing technology and to ensure that we use it in the best way to achieve maximum efficiency. We will look specifically at the impact of this year-on-year expansion and at how it has motivated and driven forward technology adoption within the PCT Translation Division, analysing how we have managed to get more work done without exponentially increasing the number of translators and the budget allocation. Responsible for the translation of documents related to the international patent filing process, we have witnessed significant growth, with the number of words requiring translation per year increasing from approximately 57 million in 2007 to 150 million in 2017 and there are no signs that this is going to slow down any time soon. In this paper we shall present the PCT Translation Division of the World Intellectual Property Organization (WIPO), a specialized agency of the United Nations.
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