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nmt_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 […]

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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). […]

nmt_weekly

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 […]

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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 […]

nmt_weekly

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 […]

nmt_weekly

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 […]

nmt_weekly

Issue #1 – Scaling Neural MT

Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic Training a neural machine translation engine is a time consuming task. It typically takes a number of days or even weeks, when running powerful GPUs. Reducing this time is a priority of any neural MT developer. In this post we explore a recent work (Ott […]

nmt_weekly

Introducing “The Neural MT Weekly”!

The field of Machine Translation is moving at as fast a pace as we’ve ever seen. Month on month, there is an increase in the number of research papers being published, with the majority obviously focusing on Neural MT. As a company at the forefront of this technology, it’s critcally important that we at Iconic […]

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TAUS QE Summit – Dublin – April 11

An upcoming event in our spring schedule is the TAUS QE Summit, taking place in our home town of Dublin, Ireland on April 11th.  This one day meeting, hosted by Microsoft, will focus on translation quality evaluation and related themes. Iconic’s CEO, John Tinsley, will again be speaking at this year’s Summit alongside colleagues from Dell, Welocalize, […]

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Relativity Fest – London – May 1

Stay tuned! Details to follow.

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