July 25, 2019 Multilingual e-disclosure, NLP Leave a comment

Issue #47 – It’s all French Belgian Fries to me, or The Art of Multilingual e-Disclosure (Part I)

25 Jul19 Issue #47 – It’s all French Belgian Fries to me, or The Art of Multilingual e-Disclosure (Part I) Author: Jérôme Torres Lozano, Director of Professional Services, Inventus Over the next two weeks, we’re taking a slightly different approach on the blog. In today’s article, the first of two parts, we will hear from Jérôme Torres-Lozano of Inventus, a user of Iconic’s Neural MT solutions for e-discovery. He gives us an entertaining look at his experiences on the challenges of language, […]

Issue #46 – Augmenting Self-attention with Persistent Memory

18 Jul19 Issue #46 – Augmenting Self-attention with Persistent Memory Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In Issue #32 we introduced the Transformer model as the new state-of-the-art in Neural Machine Translation. Subsequently, in Issue #41 we looked at some approaches that were aiming to improve upon it. In this post, we take a look at significant change in the Transformer model, proposed by Sukhbaatar et al. (2019), which further improves its performance. Each Transformer layer consists of two types […]

July 11, 2019 NLP, Robustness in Neural MT Leave a comment

Issue #45 – Improving Robustness in Real-World Neural Machine Translation

11 Jul19 Issue #45 – Improving Robustness in Real-World Neural Machine Translation Author: Dr. John Tinsley, CEO & Co-founder @ Iconic Next month, the 17th Machine Translation Summit will take place in Dublin, Ireland and the Iconic team will be in attendance. Not only that, we will be presenting our own work – Gupta et al. (2019) – on some of the steps we take to improve the robustness, stability, and quality of the Neural MT engines that we run […]

July 4, 2019 NLP, Tagged Back-Translation Leave a comment

Issue #44 – Tagged Back-Translation for Neural Machine Translation

04 Jul19 Issue #44 – Tagged Back-Translation for Neural Machine Translation Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Note from the editor: You may have noticed that our posts have been a little more technical in nature over the last few weeks. This is reflective of a broader trend in R&D whereby higher level topics and “breakthroughs” have been covered, and scientists are now drilling down to optimise existing approaches. This can be seen again in today’s on […]

June 27, 2019 NLP, Overcorrection Recovery Leave a comment

Issue #43 – Improving Overcorrection Recovery in Neural MT

27 Jun19 Issue #43 – Improving Overcorrection Recovery in Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic In Neural MT, at training time, the model predicts the current word with the ground truth word (previous word in the sequence) as a context, while at inference time it has to generate the complete sequence. This discrepancy in training and inference often leads to an accumulation of errors in the translation process, resulting in out-of-context translations. In this post we’ll discuss […]

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 […]

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