Issue #41 – Deep Transformer Models for Neural MT

13 Jun19 Issue #41 – Deep Transformer Models for Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic The Transformer is a state-of-the-art Neural MT model, as we covered previously in Issue #32. So what happens when something works well with neural networks? We try to go wider and deeper! There are two research directions that look promising to enhance the Transformer model: building wider networks by increasing the size of word representation and attention vectors, or building […]

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Issue #40 – Consistency by Agreement in Zero-shot Neural MT

06 Jun19 Issue #40 – Consistency by Agreement in Zero-shot Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic In two of our earlier posts (Issues #6 and #37), we discussed the zero-shot approach to Neural MT – learning to translate from source to target without seeing even a single example of the language pair directly. In Neural MT, the zero-shot training is achieved using multilingual architecture (Johnson et al. 2017) – a single NMT engine that can translate between […]

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Issue #39 – Context-aware Neural Machine Translation

30 May19 Issue #39 – Context-aware Neural Machine Translation Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic Back in Issue #15, we looked at the topic of document-level translation and the idea of looking at more context than just the sentence when machine translating. In this post, we will have a look more generally at the role of context in machine translation as relates to specific types of linguistic phenomena and issues related to them. We review the work […]

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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 # 33 – Neural MT for Spoken Language Translation

11 Apr19 Issue # 33 – Neural MT for Spoken Language Translation Author: Dr. Marco Turchi, Head of the Machine Translation group at Fondazione Bruno Kessler – FBK , Italy This week, we have a guest post from Dr. Marco Turchi, head of the Machine Translation Group at Fondazione Bruno Kessler (FBK) in Italy. Marco and his team have a very strong pedigree in the field, and have been at the forefront of academic research into Neural MT. In this issue, […]

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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 #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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