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nmt_weekly

Issue #9 – Domain Adaptation for Neural MT

Author: Raj Nath Patel, Machine Translation Scientist @ Iconic While Neural MT has raised the bar in terms of the quality of general purpose machine translation, it is still limited when it comes to more intricate or technical use cases. That is where domain adaptation — the process of developing and adapting MT for specific […]

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Issue #8 – Is Neural MT on par with human translation?

Author: Dr. John Tinsley, CEO @ Iconic The next few articles of the Neural MT Weekly will deal with the topic of quality and evaluation of machine translation. Since the advent of Neural MT, developments have moved fast, and we have seen quality expectation levels rise, in line with a number of striking proclamations about […]

nmt_weekly

Issue #7 – Terminology in Neural MT

Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic In many commercial MT use cases, being able to use custom terminology is a key requirement in terms of accuracy of the translation. The ability to guarantee the translation of specific input words and phrases is conveniently handled in Statistical MT (SMT) frameworks such as Moses. […]

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Issue #6 – Zero-Shot Neural MT

Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic As we covered in last week’s post, training a neural MT engine requires a lot of data, typically millions of sentences in both languages which are aligned at the sentence level, i.e. every sentence in the source (e.g. Spanish) has a corresponding target (e.g. English). […]

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Issue #5 – Creating training data for Neural MT

Author: Prof. Andy Way, Deputy Director, ADAPT Research Centre This week, we have a guest post from Prof. Andy Way of the ADAPT Research Centre in Dublin. Andy leads a world-class team of researchers at ADAPT who are working at the very forefront of Neural MT. The post expands on the topic of training data […]

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Issue #4 – Six Challenges in Neural MT

Author: Dr. John Tinsley, CEO @ Iconic A little over a year ago, Koehn and Knowles (2017) wrote a very appropriate paper entitled “Six Challenges in Neural Machine Translation” (in fact, there were 7 but only 6 were empirically tested). The paper set out a number of areas which, despite its rapid development, still needed […]

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