Issue #37 – Zero-shot Neural MT as Domain Adaptation

16 May19 Issue #37 – Zero-shot Neural MT as Domain Adaptation Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Zero-shot machine translation – a topic we first covered in Issue #6 –  is the idea that you can have a single MT engine that can translate between multiple languages. Such multilingual Neural MT systems can be built by simply concatenating parallel sentence pairs in several language directions and only adding a token in the source side indicating to which […]

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Issue #36 – Average Attention Network for Neural MT

09 May19 Issue #36 – Average Attention Network for Neural MT Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In Issue#32, we covered the Transformer model for neural machine translation which is the state of the art in neural MT. In this post we explore a technique presented by Zhang et. al. 2018, which modifies the transformer model and speeds up the translation process by 4-7 times across a range of different engines. Where is the bottleneck? In the […]

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Issue #35 – Text Repair Model for Neural Machine Translation

02 May19 Issue #35 – Text Repair Model for Neural Machine Translation Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Neural machine translation engines produce systematic errors which are not always easy to detect and correct in an end-to-end framework with millions of hidden parameters. One potential way to resolve these issues is doing so after the fact – correcting the errors by post-processing the output with an automatic post-editing (APE) step. This week we take a look at […]

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Issue #34 – Non-Parametric Domain Adaptation for Neural MT

25 Apr19 Issue #34 – Non-Parametric Domain Adaptation for Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic In a few of our earlier posts (Issues #9 and #19) we discussed the topic of domain adaptation – the process of developing and adapting machine translation engines for specific industries, content types, and use cases – in the context of Neural MT. In general, domain adaptation methods require retraining of neural models, using in-domain data or infusing domain information at the […]

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Issue #32 – The Transformer Model: State-of-the-art Neural MT

04 Apr19 Issue #32 – The Transformer Model: State-of-the-art Neural MT Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In this post, we will discuss the Transformer model (Vaswani et al. 2017), which is a state-of-the-art model for Neural MT. The Transformer model was published by Google Brain and Google Research teams in June 2017 and has been a very popular architecture since then. It does not use either Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN). Instead, […]

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Issue #31 – Context-aware Neural MT

28 Mar19 Issue #31 – Context-aware Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic In this week’s post, we take a look at ‘context-aware’ machine translation. This particular topic deals with how Neural MT engines can make use of external information to determine what translation to product – “external information” meaning information other than the words in the sentence being translated. Other modalities, for instance speech, images, and videos, or even other sentences in the source document […]

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Issue #30 – Reducing loss of meaning in Neural MT

28 Mar19 Issue #30 – Reducing loss of meaning in Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic An important, and perhaps obvious feature of high-quality machine translation systems is that they preserve the meaning of the source in the translation. That is to say, if we have two different source sentences with slightly different meanings, we should have slightly different translations. However, this nuance can be a challenge, even for state-of-the-art systems, particularly in cases where source […]

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Issue #28 – Hybrid Unsupervised Machine Translation

07 Mar19 Issue #28 – Hybrid Unsupervised Machine Translation Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic In Issue #11 of this series, we first looked directly at the topic of unsupervised machine translation – training an engine without any parallel data. Since then, it has gone from a promising concept, to one that can produce effective systems that perform close to the level of fully supervised engines (trained with parallel data). The prospect of building good MT engines […]

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Issue #27 – Use case: Neural MT for the Life Sciences

27 Feb19 Issue #27 – Use case: Neural MT for the Life Sciences Author: Dr. John Tinsley, CEO @ Iconic Neural MT has had quite a significant impact on how global enterprises are looking at translation automation to improve existing workflows. Above and beyond that, however, organisations are considering how machine translation can transform key areas of their business. The reasons for this are clear – when adapted effectively for a particular use case, Neural MT can produce significantly better, […]

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Issue #26 – Context and Copying in Neural MT

21 Feb19 Issue #26 – Context and Copying in Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic When translating from one language to another, certain words and tokens need to be copied, and not translated, per se, in the target sentence. This includes things like proper nouns, names, numbers, and ‘unknown’ tokens. We want these to appear in the translation just as they were in the original text. Neural MT systems with subword vocabulary are capable of copying […]

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