November 23, 2018 machine translation, NLP Leave a comment

Issue #18 – Simultaneous Translation using Neural MT

23 Nov18 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 on Slator. In this week’s post, […]

November 15, 2018 Neural MT, NLP Leave a comment

Issue #17 – Speeding up Neural MT

15 Nov18 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 expensive) graphics processors (GPUs), which is […]

November 8, 2018 Neural MT, NLP Leave a comment

Issue #16 – Revisiting synthetic training data for Neural MT

08 Nov18 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, with a few million […]

October 25, 2018 Neural MT, NLP Leave a comment

Issue #14 – Neural MT: A Case Study

25 Oct18 Issue #14 – Neural MT: A Case Study 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?”. While all new projects are built using Neural MT, there is a certain element of – “if it ain’t broke, don’t fix it” – that can creep in when it comes to […]

September 5, 2018 human translation, NLP Leave a comment

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

05 Sep18 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 translation” […]

August 30, 2018 decoding, NLP Leave a comment

Issue #7 – Terminology in Neural MT

30 Aug18 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 steps, we can interject and specify directly […]

July 25, 2018 Data Cleaning, NLP Leave a comment

Issue #2 – Data Cleaning for Neural MT

25 Jul18 Issue #2 – Data Cleaning for Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic “Garbage in, Garbage out” – noisy data is a big problem for all machine learning tasks, and MT is no different. By noisy data, we mean bad alignments, poor translations, misspellings, and other inconsistencies in the data used to train the systems. Statistical MT systems are more robust, and can cope with up to 10% noise in the training data without […]

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