#189 Volume 31 Issue 3 May/June 2020

Would you introduce yourself?
Wafaa Mohiy, general manager of the Saudisoft Co. Ltd. Egypt office.

Where do you live?
Cairo, Egypt.

How did you get started in this industry?
It has been a long journey. I graduated with a computer science major and worked as a business application software developer for the first three years of my career. It was a good start, but I did not find my passion in developing business applications. I took systems engineering courses and afterwards I specialized in enabling applications/OS to support Arabic languages, as there were not many back then!

Iconic Translation Machines Ltd. http://iconictranslation.com

Amplexor www.amplexor.com

Comunica www.comunicatranslations.com

Content Connections 2020

May 18, 2020, Online

Acrolinx, www.acrolinx.com/cc

TAUS Industry Leaders Forum*

June 9-10, 2020, Amsterdam, Netherlands

TAUS, https://bit.ly/38ALMgM

Translation, Transcreation, Transadaptation and the Science Behind Them*

June 18, 2020, San Jose, California USA

International Multilingual User Group www.meetup.com/IMUG-Silicon-Valley/events/bplhmrybcjbxb

(* = still scheduled at time of print)

World Savvy

When I first became the consul for Latvia in the US State of Minnesota, right on the border with Canada, I felt confident that I knew a lot about Latvia’s regions, culture and language. Latvian is one of the EU’s 23 official languages and many LSPs offer it — there is machine translation and AI software available in Latvian.

But another language in Latvia, Latgalian, long thought to be dying, is seeing a rebirth and provides a case history on how other troubled languages and dialects can survive.

Perspectives

The speed at which software and artificial intelligence (AI) is maturing is awe-inducing. One of my favorite examples of real-world AI is Tesla Autopilot. It’s an advanced driver-assistance system that has lane centering, adaptive cruise control, self-parking, automatic lane change and even the ability to navigate autonomously on limited access freeways. But Tesla also recommends that drivers keep their hands on the wheel when using these powerful automation features.

Perspectives

Time is a funny thing. It’s hard to believe five years have already elapsed since the promising early results of applying deep learning and artificial neural networks to machine translation (MT). Yet it’s equally hard to believe it’s only been that long — that a radically new method of approaching one of computer science’s oldest challenges, automated translation, could so quickly and completely usurp statistical machine translation.

Since our company started doing post-editing at scale in 2001, we now have hundreds of millions of words that allow us to track trends in MT quality and the amount of changes that professional translators have to make to MT output to create quality translations. The data confirms what readers likely already know: carefully trained neural machine translation (NMT) produces draft translations that require substantially less editing.

Perspectives

Since its foundation as an academic discipline in 1955, the AI research field has been divided into different camps: symbolic AI and machine learning. While symbolic AI used to dominate in the first decades, machine learning has been very trendy lately, so let’s try to understand each of these approaches and their main differences when applied to natural language processing (NLP).

Machine learning is an application of AI where statistical models perform specific tasks without using explicit instructions, relying instead on patterns and inference. Machine learning algorithms build mathematical models based on training data in order to make predictions.

here are many areas in the translation business that may benefit from implementation of AI and particularly from machine learning (ML). Since AI is not completely predictable, implementation of any kind of AI also involves new risks. The introduction of AI may produce an unexpected impact or a positive breakthrough.

In linguistics, we have been seeing a continuous evolution of machine translation (MT), the most evident example of ML. We already know what to expect from it, what the benefits are, and how to address the risks. How we manage it also presents a great potential for applying ML to make improvements elsewhere. This includes automated decision-making based on acquired data to either provide human workers with pointers, or to eliminate certain human management in order to resolve bottlenecks in continuous development or continuous translation. We could also use ML for making predictions and bringing to light potential risks (of missing a deadline, losing a client) and maybe to propose corrective measures.

here are many ways we are seeing AI used today in our customer and partner ecosphere. Localization professionals will be familiar with some of the basic AI building blocks and current applications, but there may be new ideas for AI paths you may want to pursue in the future, whether for your current job or a future one. After all, “AI is going to have as big an impact on our society as electricity,” according to Risto Siilasmaa, chairman of the board at Nokia.

In Figure 1, we show some of the most commonly used AI-related terms and how they fit together. Computer science is depicted as the general study of computers and computing concepts. Although the exact definition of AI is a matter of debate, for this discussion let’s define AI as systems that can perform complex tasks autonomously and adaptively; that is, without constant guidance by a user and with the ability to improve performance by learning from experience. Machine learning is a process by which systems improve their performance in a given task with more and more experience or data. Deep learning is a class of machine learning algorithms that uses multiple layers to calculate high-level outputs from raw inputs. With the increasing power of modern computers, more and more layers are added for greater complexity.

achine translation (MT) is a constantly evolving field, where research contributions from natural language processing, computational linguistics, artificial intelligence and computer science merge together with the aim of improving the quality and reliability of automatically translated texts. Several different MT approaches have showcased their advantages and limitations so far, thus motivating the adoption of multi-engine MT (MEMT), where multiple MT systems are queried at the same time in order to provide the best translation alternative.

However, significant costs, scarce automation and the risk of nonoptimized configurations make this promising research area less of a viable solution for global companies. There is a need for MEMT management systems capable of autonomously suggesting the optimal MT engine for the given language pair and domain to a translation company.

achine translation (MT) technology can be divided broadly into two very different approaches. The older technology relies on close integration of human knowledge about language on one hand, and on the calculation speed and power of the computer on the other hand. For a long time, this approach was considered the only feasible one. However, the advent of the internet and the increased power of computers made it possible to develop a very different approach to MT. The development led to various statistical approaches, where machine learning is key. The latest versions of the statistical approaches are known as neural machine translation. While it is difficult to fully understand how this system works, its development has currently usurped almost all resources in the study of machine translation.

The current situation is very unfortunate, because roughly 1% of the world’s languages fulfill even the minimal criteria for statistical or neural machine translation. This trend pushes the majority of languages into a marginal position, because if the language is not computerized, as the customary expression runs, it is not respected.

ndian Sign Language (ISL), also known as Indo-Pakistani Sign Language (IPSL), is one of the most common sign languages in South Asia, used by millions of speakers in India, Pakistan and Bangladesh. However, as is the case with some sign languages, it is difficult to accurately estimate the number of people who practice ISL. The study on sign languages in India meets a massive roadblock in particular, as the actual number of deaf people has not been ascertained. This barrier creates an enormous problem for individuals and organizations who want to have new technologies implemented around the usage of sign languages in public spheres.

The 2011 Indian census stated that roughly 1.3 million people in India experience hearing impairment. These numbers are quite different from that of India’s National Association of the Deaf, which puts this number at 18 million, or roughly 1% of India’s population. But even these numbers are low when they are compared to global standards — it has been reported that 5% of the world’s population and 3.5% of Americans experience hearing loss.

n June 2019, Politico reported on the travails of Epic, a leading US maker of digital health software, in adapting its product to the requirements of the Danish health system. The company discovered that translating the user interface was far from enough. Although some of the problems Danish healthcare workers faced were the result of subpar translation, others were more fundamental. For example, a lot of Epic’s functions were around customer billing, but in the Danish single-payer system, these features were irrelevant. Similarly, the system had hard-coded roles for nurses and doctors based on US legal requirements that prevented them from doing their job in Denmark. One physician described the results as “indescribable, total chaos” and Politico reports that the same physician found that “Epic might work in the United States […] but its design was so hard-coded in US medical culture that it couldn’t be disentangled.” After the Danish government spent over $500 million over three years, the system still is not fully functional.

s technology advances, users are becoming more demanding and it is becoming harder to meet instant service expectations from customers. Companies also face the challenge of multilingualism as they are pressured to bridge language barriers and provide content in the customer’s native language. Thankfully, AI and machine learning (ML) have created opportunities for companies to deliver a consistent user experience across languages by using AI-enabled, intelligent virtual assistants. Deep machine learning and language data are two major components that can make or break these human-like interactions. Chatbots not only enable companies to serve their customers 24/7, but also create an exciting and emerging field for the localization industry.

Two decades ago, I began studying the globalization of websites. At the time, the practice was — like the internet itself — in its infancy and there were few established best practices. But by talking to the people who managed global websites and managing localization projects myself, I began to learn which practices worked and which did not. This led to the formation of an annual benchmarking report known as the Web Globalization Report Card. Now in its 16th edition, this ranking of the leading global websites highlights those websites that excel across a number of categories, including global reach, consistency and depth of localization.

Takeaway

The need for multilingual individuals is growing at a rapid pace in our workforce, in the public sector and, most importantly, in our classrooms — be they virtual or in-person. We are living in a more interconnected and interdependent world — a world where knowing a second (or even third) language is critical to being successful in a 21st-century global economy.

According to a 2019 survey by ACTFL, nearly one in four US employers acknowledged losing or being unable to pursue a business opportunity over a singular lack of language skills.

Statistics like these indicate an alarming trend. As the world language skills gap in the US economy continues to grow, the number of qualified teachers continues to fall. The language education profession is facing incredible challenges when it comes to teacher recruitment and retention. As of 2018, 44 states and Washington, D.C. currently report shortages of qualified language teachers.