Tag: Machine Translation

What makes SDL Machine Translation “state-of-the-art”?

What makes SDL Machine Translation “state-of-the-art”?

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SDL Machine Translation is an enterprise-grade solution for those looking to apply the latest in neural machine translation to automatically translate content. With over 20 years of experience, SDL has used the latest advances in the field of artificial intelligence to create a solution that helps organizations break the language barriers for content-intensive processes securely, at scale.

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Secure Machine Translation Minimizes Business Risks for USD 10B Technology Company

Secure Machine Translation Minimizes Business Risks for USD 10B Technology Company

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Even before the rise of neural networks, machine translation (MT) has played a significant role for multinational companies, and not just for customer-facing content.

Global companies need to communicate effectively in the native languages of not just their customers but also their employees and partners. This was the problem faced by a large publicly traded global technology company nearly a decade ago.

The company revenues register over USD 10bn with an operational footprint across 30 countries serving 100,000 customers.

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Ethics in Machine Translation [Podcast]

Ethics in Machine Translation [Podcast]

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We’re in a localization and globalization market now where more words are translated every day through machine translation than what was translated in the entire human language corpus in the past.

Not only does such a massive amount of machine translation radically change the role of human translators, it also creates a whole new range of issues that impact the translation and globalization paradigm itself.

And one of the most important issues is ethics.

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MT Isn’t Good For Languages, But..

MT Isn’t Good For Languages, But..

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In a Sci-Fi movie we all know, there is the machine invention merging in to fulfill a certain demand and a great need covering all the routine and dangerous work. But, there is this end; that we all know by heart as well, the machine evolves and revolts against the maker and works for the maker’s destruction. This shows us the double edged weapon that the machine has. In this article we will reflect these edges: pros and cons, of the machine translation.

The machine has been always a great tool in saving time and money for the sake of exerting the effort to enhance it to produce a better quality. Luckily, we have achieved this, and the machine translation has saved a lot of time translating repetitive content such as the financial field that is full of numbers and the technical content that is full of saved terminology,…etc. Machine translation also grants accuracy translating such content, where no complex structure or ambiguity surrounds the content. It also translates fast, which means less time; and in a world where time means money; this is the best to rely on.

Such applications also evolved to translate images and signs on the spot. Imagine you’re lost in a country you don’t speak its language! No one to understand, no one to ask!

If a programmer or a developer wants to make his application or software or even a website to be used in the whole world, what would he do? Get translators from all the whole world and pay them and get broke before he even takes off?

Machine translation is the answer here as well, as it will be of a great help to this programmer because such a repetitive content will can be translated in a good quality.

On the other hand, the climax of the movie emerges. And we come to the question: does it scheme to destroy the translation profession and the death of the language?

Did it prove high quality in the artistic translation? No, why?

This is due some factors, which are:

  • Perplexity

Language is an evolving living entity. One of the language schools measured the elevation of a language by its ability to complicate. The more complicated the structure and the diction, the higher this language.

Unfortunately, machine translation lacks the ability to deal or understand such complication. This, by turn lead us to the length ratio.

  • Length Ratio

After much trainings and tests to the machine translation followed by evaluation, it appears that the produced is more of chopped sentences, cut off, simple, and normalized affected by the source. This also relates to the language complication; consequently, it defies the language elevation and richness. And speaking of richness, we go to

  • Lexical Density

According to MacMillan Dictionary, it is: “the proportion of content words to function words in a text. The higher the proportion of content words, the greater the lexical density”.

Machine translation is effective at these types of texts that don’t really have many of content words, and it gets sometimes lost the more content words existed. This also applies to the diverse significant word in a translated text, which adds to the richness and the uniqueness of the language. How does the machine translation work with this?

The Machine works with the most frequent words, to keep the text consistent; which threatens the less frequent words with death and oblivion.

However, don’t get disappointed dear linguist, there is this MT Summit that gets to be held globally every 2 years, and regionally every year. They discuss such issues and they try to find solutions for it based on research papers and tests with evaluations. Furthermore, we have you to post-edit these translations, creatively. Machine translation will never replace you, it’s just you who will upgrade and drench yourself more in your language linguistics.

Here, we reach a conclusion that tells us that machine translation is not scheming to the linguists death, unless they allowed, for sure. Nevertheless, it might affect the language.

You do what you got to do.

Six Challenges for Neural Machine Translation

Six Challenges for Neural Machine Translation

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Abstract

We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

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Quantisation of Neural Machine Translation models

Quantisation of Neural Machine Translation models

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When large amounts of training data are available, the quality of Neural MT engines increases with the size of the model. However, larger models imply decoding with more parameters, which makes the engine slower at test time. Improving the trade-off between model compactness and translation quality is an active research topic. One of the ways to achieve more compact models is via quantisation, that is, by requiring each parameter value to occupy a fixed number of bits, thus limiting the computational cost. In this post we take a look at a paper which achieves 4 times more compact Transformer Neural MT models via quantisation into 8 bit values, with no loss in translation quality according to BLEU score.

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Issue #55 – Word Alignment from Neural Machine Translation

Issue #55 – Word Alignment from Neural Machine Translation

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Word alignments were the cornerstone of all previous approaches to statistical MT. You take your parallel corpus, align the words, and build from there. In Neural MT however, word alignment is no longer needed as an input of the system. That being said, research is coming back around to the idea that it remains useful in real-world practical scenarios for tasks such as replacing tags in MT output.

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Evaluating machine translation in a low-resource language combination

Evaluating machine translation in a low-resource language combination

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Aim
• Main aim:
• Determining which type of MT system (RBMT, PBMT
or NMT) is perceived as more adequate in the
context of a minoritized language such as Galician in
a MT+PE workflow.
3
• Specific aims:
• BLEU automatic evaluation.
• Human evaluation (quality perception survey
conducted among experienced professional posteditors)
• Error analysis framework (MQM)
Evaluating machine translation in a low-resource
language combination: Spanish-Galician

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