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Introduction to Statistical Machine Translation, Post-Editing and AdaptiveMT – Presentation at Cardiff University by Valeria Filippello 08.03.17

Valeria Filippello is a Principal Computational Linguist for SDL, a leading translation technology company. This presentation, an Introduction to Statistical Machine Translation, Post-Editing and AdaptiveMT, was an NSS Careers and Networking Event, organised jointly by the School of Modern languages (MLANG), Cardiff University, and ITI Cymru Wales. Valeria has spent 10 years in her role, her primary task being to test and develop machine translation (MT) and also to train translators in the use of MT. She is a trained translator and interpreter. Why the need for MT? Ability to handle content explosion – for example the launch of a new mobile phone is done very quickly and there needs to be quick translations done for any product release Reduced production costs Faster throughput Greater industry acceptance There is also a greater consumer acceptance of MT. An estimated 75% of web users use free MT tools due to the greater accessibility and integration of MT solutions. 93% of these MT users use it to understand English. Over 90% are non-English speakers. Valeria went on to describe the different types of MT technologies… There is RBMT – Rules-based Machine Translation – the Engine consists of a set of rules, each written by a linguist. RBMT is time-consuming and it’s application is limited. There is SMT – Statistical Machine Translation – on the basis of a large set of examples, the engine learns translation rules for itself. Also, there is Hybrid MT which is any combination of SMT and RBMT Technology. SMT involves a large database where ‘the system “learns” how to translate by analysing statistical relationships between source and target data.’ The Pros and Cons of SMT ADVANTAGES Once the learning system is in place, developing new engines is a quick process Translations are relatively fluent and show some context-sensitivity DISADVANTAGES Needs large databases of good quality to be feasible engine cannot be influenced directly little control on terminology SMT provides solutions for Post-Editing (PE). Post-editing is an ‘en vogue’ category of employment for translators these days. PE is more and more accepted by translators. PE needs to be sustainable in the long term You need the right solution and a good PE process There is a challenge in devising SMT solutions for Post-Editing. Train translators to become efficient post-editors Retrain MT engines taking into account post-editors’ feedback Here are some types of SMT solutions BASELINES eg. Google Translate VERTICALS –…

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