11 Oct18
Author: Raj Nath Patel, Machine Translation Scientist @ Iconic What are the different approaches to Neural MT? Since its relatively recent advent, the underlying technology has been based on one of three main architectures:...
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The Neural MT Weekly
04 Oct18
Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Most flavours of Machine Translation naturally use the word as the basis for learning models....
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27 Sep18
Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In this week's article, we will explore unsupervised machine translation. ...
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20 Sep18
Author: Dr. Joss Moorkens, Assistant Professor, Dublin City University This week, we have a guest post from Prof. Joss Moorkens of Dublin City University. ...
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13 Sep18
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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05 Sep18
Author: Dr. John Tinsley, CEO @ IconicThe 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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30 Aug18
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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22 Aug18
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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15 Aug18
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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08 Aug18
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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