Knowledge

Issue-18-Simultaneous-Translation-using-Neural-MT

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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Issue-17-–-Speeding-up-Neural-MT

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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Issue-16-Revisiting-synthetic-training-data-for-Neural-MT

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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Issue-15-Document-Level-Neural-MT

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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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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