MT has come a long way. After seventy years of research, the technology is now taken into production. And yet, we are missing out on the full opportunities. Because the developers are preoccupied with the idea that the massive models will magically solve the remaining problems. And because the operators in the translation industry are slow in developing new MT-centric translation strategies. This article is an appeal to everyone involved in the translation ecosystem to come off the fence and realize the full benefits of MT. We can do better!

It doesn’t happen very often nowadays, but every now and then I still find in my inbox a great example of what is becoming a relic from the past: a spam email with cringy translation. Like everyone else, I’m certainly not too fond of spam, but the ones with horrendous translations do get my attention. The word-by-word translation is like a puzzle to me: I want to know if I can ‘reverse-translate’ it to its original phrasing.

Nowadays, Machine Translation (MT) is increasingly being considered as another asset in the translation industry along with the translation memories (TM) and the terminologies. Language Services Providers (LSP) are gradually pondering the benefits of MT in their projects and either creating a specific department for MT matters, partnering with major MT engine providers, or trusting their client’s MT results. Thus, the usage of MT is a global fact, but its application in particular contexts still has to be explored.

In another article, we discussed automatic machine translation (MT) evaluation metrics such as BLEU, NIST, METEOR, and TER. These metrics assign a score to a machine-translated segment by comparing it to a reference translation, which is a verified, human-generated translation of the source text. As a result, they are easy to understand and work with, but severely limited by their reliance on references, which are not always available in a translation production setting.

Automatic evaluation of Machine Translation (MT) output refers to the evaluation of translated content using automated metrics such as BLEU, NIST, METEOR, TER, CharacTER, and so on. Automated metrics emerged to address the need for objective, consistent, quick, and affordable assessment of MT output, as opposed to a human evaluation where translators or linguists are asked to evaluate segments manually. 

The language technology innovator and service provider Tilde has developed considerable expertise in providing language services to the Presidency of the Council of the European Union. We caught up with Mārcis Pinnis, Chief AI Officer, and Artūrs Vasiļevskis, Head of Machine Translation, to find out more about the challenges and promises of this ongoing project.

Machine translation (MT) technology has been around for seven decades now. It is praised for its speed and cost-effectiveness, and its quality has gotten a lot better too since the arrival of neural machine translation (NMT). Higher throughput, quicker turnaround time, and the need to reduce overall cost are the main reasons for implementing MT in almost every case. Sounds great, right? Still, to understand how to implement machine translation to meet your translation needs, you should first consider a few factors.

The year 2020 is set to be the most difficult for most of us in decades. While the future of the translation industry after the COVID-19 pandemic depends on many factors, there is no doubt that its technologies are set to evolve radically. Indeed, in many ways, the machine translation (MT) journey is just beginning: the end of 2019 and the beginning of 2020 were full of fresh, eye-opening perspectives on tomorrow’s MT and other language technologies.

How should the translation industry engage with the current conversation about ethical concerns in technology use? Here are some preliminary notes for an answer.

On 11 July 2019, Google’s AI team has published their recent research titled Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges. A team of 13 researchers has worked on “building a universal neural machine translation (NMT) system capable of translating between any language pair.” The model is “a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples.”

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). The events that brought all of them together were: EMNLP 2018, one of the biggest conferences on Natural Language Processing in the world, and WMT 2018, which for many years has been one of the most reputable conferences in the field of machine translation (MT).

What is the single most important element that is present in every translation or localization workflow? There is one process that cannot be eliminated from any type of translation or localization task, which is evaluation. This process becomes even more important when it comes to machine translation (MT).

The MT Evaluation Dilemma

The translation industry is adopting machine translation (MT) in increasing numbers. Yet a prerequisite for efficient adoption, the evaluation of MT output quality, remains a major challenge for all. Each year there are many research publications investigating novel approaches that seek ways to automatically calculate quality. The handful of techniques that have entered the industry over the years are commonly thought to be of limited use.

This article is the first in a series we'll be posting, exploring the topic of machine translation. Powering the SmartCAT ecosystem with flexible third-party integration capabilities, we’re preparing to enter the Convergence era, in line with the insightful outlook* Jaap van der Meer presented on TAUS in 2013. Not just evolve with the industry but take the lead in this process, that’s what we’re up to.

*Read Jaap’s article on TAUS here.

Nowadays, in one way or another, machine translation (MT) is part of our everyday lives. Most likely Google made that happen, about a decade ago, by launching Google Translate, a free instant online general-purpose translator allowing users to translate any text (words, phrases, documents, web pages) in different language directions.

The last significant breakthrough in the technology of statistical machine translation (SMT) was in 2005. That year, David Chiang published his famous paper on hierarchical translation models that allowed to significantly improve the quality of statistical MT between distant languages. Nowadays we are standing on the verge of an even more exciting moment in MT history: deep learning (DL) is taking MT towards much higher accuracy and finally brings human-like semantics to the translation process.

Neural Machine Translation (NMT) systems have achieved impressive results in many Machine Translation (MT) tasks in the past couple of years. This is mainly due to the fact that Neural Networks can solve non-linear functions, making NMT perfect for mimicking the linguistic rules followed by the human brain.

Since its launch in 2007, Google Translate has brought machine translation to the masses, making it free, quick and easy, and therefore significantly contributing to the demolition of the language barriers.