Automated Translation (MT) Trends
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Automated Translation (MT) Trends
Material related to the use and continued development of machine translation
Curated by K Vashee
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Taking Translation Metadata Beyond Translation Memory Descriptors

Taking Translation Metadata Beyond Translation Memory Descriptors | Automated Translation (MT) Trends | Scoop.it
 This is a guest post on Translation Metadata by Luigi Muzii. Some may recall his previous post: The Obscure and Controversia
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Post Editing - What does it REALLY mean?

Post Editing - What does it REALLY mean? | Automated Translation (MT) Trends | Scoop.it
While many people may consider that all post-editing is the same, there are definitely variations that are worth a closer look. This is
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LSP Perspective: Applying the Human Touch to MT, Qualitative Feedback in MT evaluation

LSP Perspective: Applying the Human Touch to MT, Qualitative Feedback in MT evaluation | Automated Translation (MT) Trends | Scoop.it
In all the discussion on MT that we hear, we do not often hear much about the post-editors and what could be done to enhance and improv
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Neural MT Technology Looking for a Business Model

Neural MT Technology Looking for a Business Model | Automated Translation (MT) Trends | Scoop.it
This is a guest post by Gábor Ugray, who drills into the possible rationale and potential logic of the recent DeepL NMT announcement
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A Fun, Yet Serious Look at the Challenges we face in Building Neural Machine Translation Engines

A Fun, Yet Serious Look at the Challenges we face in Building Neural Machine Translation Engines | Automated Translation (MT) Trends | Scoop.it
This is a guest post by Gábor Ugray on NMT model building challenges and issues. Don't let the playful tone and general sense of froli
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The Ongoing Neural Machine Translation Momentum

The Ongoing Neural Machine Translation Momentum | Automated Translation (MT) Trends | Scoop.it
This is largely a guest post by Manuel Herranz of Pangeanic, slightly abbreviated and edited from the original to make it mor
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Building Your Own Neural Machine Translation System in TensorFlow

Building Your Own Neural Machine Translation System in TensorFlow | Automated Translation (MT) Trends | Scoop.it

Google has announced a new Neural Machine Translation (NMT) tutorial for TensorFlow that gives readers a full understanding of seq2seq models and shows how to build a competitive translation model from scratch. The tutorial is aimed at making the process as simple as possible, starting with some background knowledge on NMT and walking through code details to build a vanilla system. It then dives into the attention mechanism [3, 4], a key ingredient that allows NMT systems to handle long sentences. Finally, the tutorial provides details on how to replicate key features in the Google’s NMT (GNMT) system [5] to train on multiple GPUs.


Via LT-Innovate
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LT-Innovate's curator insight, July 14, 2017 6:31 AM

Commoditising Neural MT...

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My Journey into "Neural Land"

My Journey into "Neural Land" | Automated Translation (MT) Trends | Scoop.it
This is a post, mostly written by Terence Lewis about his experience with open source NMT, that was just published in the 275th editio
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Elon Musk and linguists say that AI is forcing us to confront the limits of human language

Elon Musk and linguists say that AI is forcing us to confront the limits of human language | Automated Translation (MT) Trends | Scoop.it
In analytic philosophy, any meaning can be expressed in language. In his book Expression and Meaning (1979), UC Berkeley philosopher John Searle calls this idea "the principle of expressibility, the principle that whatever can be meant can be said". Moreover, in the Tractatus Logico-Philosophicus (1921), Ludwig Wittgenstein suggests that "the limits of my language mean the limits o
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Translation Quality -- WannaCry?

Translation Quality -- WannaCry? | Automated Translation (MT) Trends | Scoop.it
For as long as I have been engaged with the professional translation industry, I have seen that there exist great confusion and ambiguit
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Welcome to the Age of Human-Machine Collaboration

Welcome to the Age of Human-Machine Collaboration | Automated Translation (MT) Trends | Scoop.it
Predictive TM and Adaptive MT have captured the attention of our industry. Why is that? And what can we draw from their approaches to human-machine interaction?
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The Problem with BLEU and Neural Machine Translation

The Problem with BLEU and Neural Machine Translation | Automated Translation (MT) Trends | Scoop.it
There has been a great deal of public attention and publicity given to the subject of Neural Machine Translation in 2016. Whil
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Terminology Usage in Machine Translation

This is a guest post by Christian Eisold from berns language consulting. For many who have been using MT seriously already, it is clea
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BabelNet - A Next Generation Dictionary & Language Research Tool

BabelNet - A Next Generation Dictionary & Language Research Tool | Automated Translation (MT) Trends | Scoop.it
This is a guest post by Roberto Navigli of BabelNet, a relatively new "big language data" initiative that is currently a lexical-semanti
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Data Security Risks with Generic and Free Machine Translation

Data Security Risks with Generic and Free Machine Translation | Automated Translation (MT) Trends | Scoop.it
Together with all the news of catastrophic hurricane activity in the US, that we are being bombarded with recently, we also are seeing
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The Evolution in Corpus Analysis Tools

The Evolution in Corpus Analysis Tools | Automated Translation (MT) Trends | Scoop.it
This is a guest post by Ondřej Matuška, t he Sales & Marketing Manager of Lexical Computing , a company that develops a corpus an
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Transformer: A Novel Neural Network Architecture for Language Understanding

Transformer: A Novel Neural Network Architecture for Language Understanding | Automated Translation (MT) Trends | Scoop.it
Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks
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Transitioning entirely to neural machine translation

Transitioning entirely to neural machine translation | Automated Translation (MT) Trends | Scoop.it
More than 4.5 billion translations every day are now powered by neural networks, which provide better accuracy and fluency than previous models.
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The Pangeanic neural translation project - Pangeanic Blog

The Pangeanic neural translation project - Pangeanic Blog | Automated Translation (MT) Trends | Scoop.it
We describe our experience with neural machine translation and how Pangeanic has decided to shift all its efforts into neural networks and leave the statistical approach as a support technology for hybridization.
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Translation Industry Perspective: Utopia or Dystopia?

Translation Industry Perspective: Utopia or Dystopia? | Automated Translation (MT) Trends | Scoop.it
This is a follow-up post by Luigi Muzii on the evolving future of the "professional translation industry". His last post has alread
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A Closer Look at SDL's new MT announcements

A Closer Look at SDL's new MT announcements | Automated Translation (MT) Trends | Scoop.it
SDL recently announced some new initiatives in their machine translation product SDL Enterprise Translation Server (ETS). As with mos
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From Reasoning to Storytelling - The Future of the Translation Industry

From Reasoning to Storytelling - The Future of the Translation Industry | Automated Translation (MT) Trends | Scoop.it
This is a guest post by a frequent contributor to eMpTy Pages, Luigi Muzii, on the future of translation, written in what some may say i
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An Adversarial Review of “Adversarial Generation of Natural Language”

An Adversarial Review of “Adversarial Generation of Natural Language” | Automated Translation (MT) Trends | Scoop.it
[edit: some people commented that they don’t like the us-vs-them tone and that “deep learning people” can — and some indeed do — do good NLP work. To be clear: I fully agree. #NotAllDeepLearners ]…
K Vashee's insight:
When evaluating a paper about natural language, try keeping in mind that language is complex and that people have been working on it for a while. Don’t allow yourself to be bullshitted by large claims pretending to solve it, while actually doing tiny, insignificant, toy problems. Don’t be swayed by sexy models if they don’t solve a real problem, or when a much simpler, much more scalable baseline exists but is not mentioned. And don’t be impressed by the affiliations of authors on arxiv papers, or their reputations, especially when they attempt to work at a problem domain that they (and you..) don’t really understand. Try to learn the subject and think things through (or ask for someone else to review it). Look at their actual evaluations, not their claims.
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Advanced machine translation to be popularized in 5 to 10 years: Expert - People's Daily Online

Advanced machine translation to be popularized in 5 to 10 years: Expert - People's Daily Online | Automated Translation (MT) Trends | Scoop.it
Experts believe that machine translation good enough to replace advanced human translators will be widespread within five to 10 years, as language-processing technology has seen many breakthroughs in recent years, Xinhuanet.com reported on May 22.

Via Sergey Rybkin
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"Specializing" Neural Machine Translation in SYSTRAN

"Specializing" Neural Machine Translation in SYSTRAN | Automated Translation (MT) Trends | Scoop.it
We see that Neural MT continues to build momentum and that already most people agree that generic NMT engines outperform generi
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