The Neural MT 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 industries, content types, and use cases -- has a big...

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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 performance. Early claims of “bridging the gap between human and machine...

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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. Because SMT is performed as a sequence of distinct...

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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). During a typical training, the system looks at these bilingual...

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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 - originally presented as one of the "6 Challenges in...

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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 to be addressed by researchers and developers of Neural MT....

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Issue-3-Improving-vocabulary-coverage

Author: Raj Nath Patel, Machine Translation Scientist @ Iconic

Machine Translation typically operates with a fixed vocabulary, i.e. it knows how to translate a finite number of words. This is obviously an issue, because translation is an open vocabulary problem: we might want to translate any possible word! This is a particular issue for Neural MT where the vocabulary needs to be limited at the...

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