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Technologies for Translation and Interpreting: Challenges and Latest Developments

Julie Giguère

Working with Intelligent Machines

13 January 2021

Abstract:

Although Artificial Intelligence development in machine translation is leading to lower prices, higher efficiency and increasing speed of translation for businesses, there remains much to be done to create a robust system that covers all known languages and all specialised subject areas with the same level of usage quality. This is why having a data quality management system is key to utilising the technology safely. We like the analogy of “coaching” the machine . This is because NMT are “intelligent” machines and they will learn not just from the translation that the human translator produced, but also from other feedback. NMT engines learn from bilingual and monolingual data, the goal is to learn from the “segment + its post edit” pair and induce the model to better translate the next input segment. This means that, in time, the task of the linguist will involve less fixing of grammatical errors, and more checking whether the translation is correct or not, making the process of post-editor more enjoyable. We will look at the upstream and downstream tasks that the human editor can perform to improve the machine output and the mechanism of the intelligent machine.

Bio:

Holder of degrees in Specialised Translation and Law, Julie’s career saw her manage translation and communication for BMO Bank of Montreal as well as financial and legal translation projects at major Language Service Providers in France and in the UK. These roles were a natural fit for Julie, a passionate communicator who speaks fluent French, Spanish and English. Julie held responsibility of Asian Absolute, a boutique language service provider, specialising in Asian and Asean languages for the last 6 years. She led the global Sales and Operation teams. Her development duties at the company saw her grow the operation from a handful of staff to a team of more than thirty and oversaw a 100% increase in the acquisition rate of new clients, such as UN Women headquartered in Bangkok and WIPO (World International Patent Organisation). She also personally led the start-up of the company’s operations in Bangkok and Panama City. She has over 10 year professional experience in multilingual communications and AI applications in linguistics. She recently completed the Oxford University, Said Business School AI Programme. She is a regular guest speaker at Localisation and tech/AI events. She recently spoke at the ATC Summits 2017 and 2018; EUATC 2018; Connected World Summit 2018; AI & Big Data Innovation Summit 2018 in Beijing and the IP EXPO Manchester 2019.