Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Neural Machine Translation is known to be particularly poor at translating out-of-domain data. That is, an engine trained on generic data will be much worse at translating medical documents than an engine trained on medical data. It is much more sensitive to such differences than, say, Statistical MT. This problem is partially solved by domain...
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Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic The term “simultaneous translation” or “simultaneous interpretation” refers to the case where a translator begins translating just a few seconds after a speaker begins speaking, and finishes just a few seconds after the speaker ends. There has been a lot of PR and noise about some recent proclamations which were covered well in a recent article...
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Author: Raj Nath Patel, Machine Translation Scientist @ Iconic For all the benefits Neural MT has brought in terms of translation quality, producing output quickly and efficiently is still a challenge for developers. All things being equal, Neural MT is slower than its statistical counterpart. This is particularly the case when running translation on standard processors (CPUs) as opposed to faster, more powerful (but also more...
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Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic In a previous guest post in this series, Prof. Andy Way explained how to create training data for Neural MT through back-translation. This technique involves translating monolingual data in the target language into the source language to obtain a parallel corpus of "synthetic" source and "authentic" target data - so called back-translation. Andy reported interesting findings whereby,...
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Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In this week's post, we take a look at document-level neural machine translation. Most, if not all existing approaches to machine translation operate on the sentence level. That is to say, when translating a document, it is actually split up into individual sentences or segments, and they are processed independently of each other. With document-level Neural MT,...
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Author: Dr. John Tinsley, CEO @ Iconic As a machine translation provider, one of the questions we've been asking ourselves most frequently over the last 18 months has been, “When should we switch an existing production deployment to Neural MT?"....
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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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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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Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In this week's article, we will explore unsupervised machine translation. ...
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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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