Blog

NMT 97 Target Conditioned Sampling Optimising Data Selection for Multilingual Neural Machine Translation

Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction It is known that neural machine translation (NMT) is particularly tricky in the case of low-resource languages. Thus, it is not surprising that researchers are actively investigating how to improve the performance on NMT systems for low-resource languages and many approaches are currently being explored. In issue #88 of our blog we reviewed a method to use...

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Relativity Fest Chicago 2020

Dublin – 03 September 2020 – Iconic Translation Machines (Iconic), a world-leading Machine Translation (MT) software and solutions provider, announces its Silver Level sponsorship and participation at Relativity Fest 2020. Taking place the 21st – 23rd September, virtually, the Relativity Fest annual conference offers a community of e-discovery, compliance, and tech professionals a platform to work together to transform discovery. The event will have live...

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Using annotations for machine translating Named Entities

Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Introduction Getting the translation of named entities right is not a trivial task and Machine Translation (MT) has traditionally struggled with it. If a named entity is wrongly translated, the human eye will quickly spot it, and more often than not, those mistranslations will make people burst into laughter as machines can, seemingly, be very creative. To a...

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World-Drug-Safety-2020

Date: 1-2 September 2020 Location: Virtual Title: World Drug Safety Congress Americas The World Drug Safety Congress Americas will be a virtual event this year, taking place September 1 - 2. This premier conference is the most senior-level, industry-focused event for the pharmacovigilance (PV) community, globally, bringing together key players from the drug safety community to address drug safety challenges. With over 30 countries represented...

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NMT 95 Constrained Parameter Initialisation for Deep Transformers in Neural MT

Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction As the Transformer model is the state of the art in Neural MT, researchers have tried to build wider (with higher dimension vectors) and deeper (with more layers) Transformer networks. Wider networks are more costly in terms of training and generation time, thus they are not the best option in production environments. However, adding encoder layers...

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Ilta On banner

Date: 24 – 28 August Location: Virtual Title: ILTA>ON ILTACON has been renamed as ILTA>ON this year, as it takes the virtual route for the sake of public safety due to Covid-19. This virtual experience for the global legal technology community takes place 24th - 28th August, 2020. The five day conference offers insights into legal technology and professional education for those working in technology...

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Relativity-aero-UI

Relativity is rolling out their new Aero UI to much fanfare, and Iconic is pleased to announce that our translation connector app supports Relativity Aero UI. Having been tested with early users since the spring, Aero UI is now being rolled out. Relativity's e-discovery platform is used by thousands of organisations around the world to manage large volumes of data and quickly identify key issues...

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NMT 94 Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation

Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Curating corpora of quality sentence pairs is a fundamental task to building Machine Translation (MT) systems. This resource can be availed from Translation Memory (TM) systems where the human translations are recorded. However, in most cases we don’t have TM databases but comparable corpora, e.g. news articles of the same story in different languages. In this post,...

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NMT 93 Semantic Neural Machine Translation using AMR

Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Semantic representations were part of the very early Machine Translation (MT) systems, yet have had little role in recent Neural MT (NMT) systems. Given that a good translation should reflect the meaning of the source text, this seems an important area to focus on, particularly since the abstraction could potentially help handle data sparsity. In today’s blog...

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NMT 92 The Importance of References in Evaluating MT Output

Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Introduction Over the years, BLEU has become the “de facto standard” for Machine Translation automatic evaluation. However, and despite being the metric being referenced in all MT research papers, it is equally criticized for not providing a reliable evaluation of the MT output. In today’s blog post we look at the work done by Freitag et al....

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