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 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, […]

February 27, 2019 Neural MT, NLP Leave a comment

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, […]

February 21, 2019 Neural MT, NLP Leave a comment

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 […]

February 7, 2019 Neural MT, NLP Leave a comment

Issue #24 – Exploring language models for Neural MT

07 Feb19 Issue #24 – Exploring language models for Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Monolingual language models were a critical part of Phrase-based Statistical Machine Translation systems. They are also used in unsupervised Neural MT systems (unsupervised means that no parallel data is available to supervise training, in other words only monolingual data is used). However, they are not used in standard supervised Neural MT engines and training language models have disappeared from common […]

February 1, 2019 Neural MT, NLP Leave a comment

Issue #23 – Unbiased Neural MT

01 Feb19 Issue #23 – Unbiased Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic A recent topic of conversation and interest in the area of Neural MT – and Artificial Intelligence in general – is gender bias. Neural models are trained using large text corpora which inherently contain social biases and stereotypes, and as a consequence, translation models inherit these biases. In this article, we’ll try to understand how gender bias affects the translation quality and discuss a […]

January 24, 2019 Neural MT, NLP Leave a comment

Issue #22 – Mixture Models in Neural MT

24 Jan19 Issue #22 – Mixture Models in Neural MT Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic It goes without saying that Neural Machine Translation has become state of the art in MT. However, one challenge we still face is developing a single general MT system which works well across a variety of different input types. As we know from long-standing research into domain adaptation, a system trained on patent data doesn’t perform well when translating software documentation […]

January 17, 2019 Neural MT, NLP Leave a comment

Issue #21 – Revisiting Data Filtering for Neural MT

17 Jan19 Issue #21 – Revisiting Data Filtering for Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic The Neural MT Weekly is back for 2019 after a short break over the holidays! 2018 was a very exciting year for machine translation, as documented over the first 20 articles in this series. What was striking was the pace of development, even in the 6 months since we starting publishing these articles. This was illustrated by the fact […]

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 […]

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