June 13, 2019 Deep Transformer Models, NLP Leave a comment

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 […]

June 6, 2019 Neural MT, NLP Leave a comment

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 […]

May 30, 2019 Context-aware NMT, NLP Leave a comment

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 […]

May 16, 2019 Neural MT, NLP Leave a comment

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 […]

May 9, 2019 Average Attention Network, NLP Leave a comment

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 […]

May 2, 2019 Neural MT, NLP Leave a comment

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 […]

April 25, 2019 Neural MT, NLP Leave a comment

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 […]

April 11, 2019 Neural end-to-end SLT system, NLP Leave a comment

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, […]

April 4, 2019 Neural MT, NLP Leave a comment

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, […]

March 28, 2019 Context-aware, NLP Leave a comment

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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