Evaluating Machine Translation Systems

This post is the first in a series of upcoming posts focusing on the issue of quality evaluation of multiple MT systems. MT system selection has become a more important issue in recent times as users and buyers realize that potentially multiple MT systems can be viable for their needs, but would like to develop better, more informed selection procedures.

I have also just ended my tenure at SDL, and this departure will also allow my commentary and opinion in this blog to be more independent and objective, from this point onwards. I look forward to looking more closely at all the most innovative MT solutions in the market today and providing more coverage on them.  

As NMT technology matures it has become increasingly apparent to many buyers that traditional metrics like BLEU that are used to compare/rank different MT systems and vendors are now often inadequate for this purpose, even though these metrics are still useful to engineers who are focused on building a single MT system.  It is now much more widely understood that best practice involves human evaluations used together with automated metrics. This combined scoring approach is a more useful input in conducting comparative evaluations of MT systems.  To the best of my knowledge, there are very few in the professional translation world who do this well, and it is very much an evolving practice and learning that is happening now. Thus, I invite any readers who might be willing to share their insights into conducting consistent and accurate human evaluations to contact me about doing this here.

Most of the focus in the localization world's use of MT remains on MTPE efficiencies (edit distance, translator productivity), often without consideration of how the volume and useable quality might change and impact the overall process and strategy. While this focus has value, it misses the broader potential of MT and "leaves money on the table" as they say.

We should understand the questions that we are most frequently asking is: 

• What MT system would work best for our business purposes?
• Is there really enough of a difference between systems to use anything but the lowest cost vendor?
• Is there a better way to select MT systems than just looking at generic BLEU scores?

I have covered these questions to some extent in prior posts and I would recommend this post and this post to get some background on the challenges in understanding the MT quality big picture.

The COVID-19 pandemic is encouraging MT-use in a positive way. Many more brands now realize that speed, digital agility, and a greater digital presence matter in keeping customers and brands engaged. As NMT continues to improve, much of the "bulk translation market" will move to a production model where most of the work will be done by MT.  Translators who are specialists and true subject matter experts are unlikely to be affected by the technology in a negative way, but NMT is poised to penetrate standard/bulk localization work much more deeply, driving costs down as it does so.

This is a guest post and an unedited independent opinion from an LSP (Language Service Provider) and it is useful in providing us an example of the most common translation industry perspective on the subject of multiple MT system evaluations. It is interesting to note that the NMT advances over SMT are still not quite understood by some, even though the bulk of the research efforts and most new deployments have shifted to NMT. 

Most LSPs continue to stress that human translation is "better" than MT which most of us on the technology side would not argue against, but this view loses something when we see that the real need today is to "translate" millions of words a day. This view also glosses over the fact that all translation tasks are not the same. Even in 2020 most LSPs continue to overlook that MT solves new kinds of translation problems that involve speed and volume and that new skills are needed to really leverage MT in these new directions. There is also a tendency to position the choice as binary MT vs Human Translation, even though much of the evidence is pointing to new man + machine models that provide an improved production approach. The translation needs of the future are quite different from the past and I hope that more service providers in the industry start to recognize this. 

I also think it is unwise for LSPs to start building their own MT systems, especially with NMT. The complexity, cost and expertise required are prohibitive for most. MT systems development should be left to real experts who do this on a regular and continuing basis. The potential for LSPs adding value is in other areas, and I hope to cover this in the coming posts.

Source: MasterWord

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It’s not a secret that machine translation (MT) has taken the world by storm. Almost everyone now has had some experience with MT, mostly in the form of a translation app such as Google Translate being popular. But MT comes in a variety of formats and is heavily utilized by businesses and institutions all over the world.

With that in mind, which MT system is best? Since MT comes in many colors, figuratively speaking, which one should you ought to rely on if you decide to build your own MT system? We’ll also talk more about translation quality and whether or not MT is suitable for specialized translations such as medical translation; a critical field now for any active translation company in light of the current coronavirus pandemic that has the whole world at a standstill.

What is Machine Translation?

Machine Translation, or MT, is software that is capable of translating text from a source language to a translated text of the target language. Over the years, there have been multiple variations of MT, but there are three definitive types; Rules-based Machine Translation (RBMT), Statistical Machine Translation (SMT), and Neural Machine Translation (NMT). Here’s a quick rundown of their characteristics, including their pros and cons between each other;

1. RBMT

Rules-Based Machine Translation is one of the earliest forms of MT. Its algorithm is language-based, meaning for it to know how to translate one source language to the other, it must rely on input data in the form of a lexicon, grammar rules, and other linguistic fundamentals. The problem with RBMT systems is scaling it efficiently as it becomes more complicated as more language rules are added. Also, RBMT is never ideal for obscure languages with minuscule data. However, with the development of advanced MT systems over the years, RMBT has largely been superseded, in which you'll know more about its successor next.

2.  SMT

Statistical Machine Translation, compared to RBMT, is designed to translate languages from statistical algorithms. SMT works by being fed with data in the form of bilingual text corpora, SMT is programmed to identify patterns in the data and form its translations from it. Patterns in this context mean how many times a certain word/phrase appears consistently in a certain context. This probability learning model allows SMT systems to render relatively appropriate translations compared. It’s pretty much like ‘If this is how was it done, then this is how it should be done’. 

SMT also must be fed with plenty of data just like RBMT, but MT developers of which includes translation app developers prefer SMT due to its ease of setting up due to numerous open-source SMT systems available, cost-effectiveness due to free quality parallel text corpora that are available online, higher translation accuracy than RMBT, and its ease of scalability as the system grows bigger.

But just like RBMT, SMT can’t function well if it’s fed with insufficient and poorly structured parallel text corpora. That being said, it’s not that ideal to translate obscure languages.

3. NMT

Neural Machine Translation is the latest development in MT. Think of it as an upgraded version of SMT in which its abilities are now supplemented with artificial intelligence (AI), specifically deep learning. Not only is it capable of coming through data faster, but it can also produce better outputs through constant trial and error. SMT does it the same way as well but the only difference, albeit a definitive one, is that it’s able to do it much faster and more accurately. Google Translate recently made the switch in 2016 to NMT from its old SMT system.

Its deep learning capability is such a real game-changer that it’s able to accomplish what RBMT and SMT; translating obscure regional languages. That’s why Google Translate can cover over 100 languages such as Somalian and Gaelic. But its outputs are questionable, to say the least as it needs some time to learn a language that has little reliable data lying around for it to use. However, the development of NMT just goes to show how far MT overall has evolved over the years.

What Makes A Good Machine Translation (MT) System?

There have been many MT systems over the years and many still in development. The ones that happened to survive the test of time are select variants of RBMT and most variants of SMT. NMT has quickly gained popularity and will slowly replace SMT as the years go by. What’s generally expected out of a good custom-built MT system is reliability and quality of outputs, pretty much like any other product or service out there.

If you’re looking for a reliable metric, then BLEU (Bilingual Evaluation Understudy) is one of the most widely used MT evaluation metrics. BLEU ranks MT systems between 0 being the worst and 1 being the best. It rates how close the translated text is to a human. The more human-like and natural-sounding the translation is, the better the score.

That being said, every MT developer creates their system according to not only the developer’s but also a client’s specifications and linguistic needs. So not one of them is alike. But there are MT platforms that are widely used by multiple clients due to their flexibility of being adapted to the client’s needs and ease of use. But even with a variety of MT systems being developed over the years, one thing remains the same; MT systems have to learn from a lot of quality data and must be given the time to learn.

They say that machines are inherently dumb and that they’re only as good as job or data are given to them. For MT, that notion still rings true up to this day and will most likely keep ringing for decades to come. However, quality data isn’t only what makes a good MT system.

There are platforms in which MT is integrated with other processes for it to render quality or at the very least, passable translations. Indeed, MT itself is a process onto its own, but its outputs, even with deep learning capabilities, is still not up to par with that of a professional translator. MT has to be integrated with other processes, namely computer-assisted translation (CAT) tools.

There are many CAT tools but two of the most essential are a glossary tool and translation memory. A glossary is simply a database of terminologies and approved translations. It’s a very simple feature but very important as it saves up a lot of time for the translator as they don’t need to constantly look back and forth which translation is the perfect choice for the source text at hand.

A translation memory is also like a glossary, but stores phrases and sentences. It also saves the translator valuable time as many translations recycle the same language such as user manuals, marketing collateral, and etc. A translation memory also helps by providing consistent language at a given domain and language pair.

I Now Pronounce You Man and Machine

However, even with all the bells and whistles, developers can equip an MT system with, is MT alone enough? Can MT alone produce accurate and quality translations that are demanded by the clients of language services today? MT is part of the solution but doesn’t comprise the complete picture. It sounds counterintuitive, but MT is best paired with a professional translator as a means of optimizing the translation process.

This unlikely union broke the predictions of many that saw MT giving professional translators a run for their money and driving translation companies out of business. Professional translators work with CAT tools as it helps them be more churn out more words than ever before and helps them be more consistent. Why the need for speed? Domo’s latest report states that “2.5 quintillion bytes of data are created every single day”—that’s a lot of data and most of it is not in English which creates the rising demand for translation services.

Also, by having a translator work together with an MT system, the translator is doing the MT system a favor as well by constantly feeding back revisions for the MT to learn from and render better outputs and suggestions. All in all, it’s a highly productive and beneficial two-way street between a translator and an MT system.

Of course, this ‘relationship’ will be all for moot if the MT system wasn’t developed to a satisfactory standard. That being said, developers have to take into account both translation clients and translators themselves.

They have to ensure that not only will the MT system procure quality translations for clients but can also adapt to the needs of the translators using them. Being convenient to use and having a friendly UX design is one thing, but being able to incorporate the inputs of a translator and accurately replicating it in similar contexts is also another thing.

What Do Professional Translation Services Have Over MT?

Specifically, what can a translation company that hires professional translators to do better than artificial intelligence (AI)? Apart from translation quality and consistency, a professional translator has one advantage; they’re human. It may sound cliche but a human can understand nuances and no MT or AI are light years away from replicating.

Unable to Understand Emotional, Cultural, and Social Nuances

As of now, there is no MT yet that is capable of accurately understanding jokes, slang, creative expressions, and so on. The abilities of MT shine brightly with formulaic sentences and predictable language conventions. But if confronted with linguistic habits that are natural in everyday conversations, MT falls apart. This problem is made more pronounced at a global scale since every culture and society has its own way of speaking all the way down to highly distinct street lingo.

Unable to Process Linguistic Nuances

Parent languages are divided by their regional vernaculars and dialects. When someone’s trying to translate English to Spanish, it’s actually just generic Spanish with no local ‘flavoring’. But if you’re aiming for translations that resonate true to how Spanish people or how Mexican people speak, then a professional translator with native-speaking ability is who you need. No MT system now is able to comprehend, let alone translate linguistic nuances reliably.

Unable to Keep Up With Linguistic Trends

Languages change every day with new words being constantly added and removed to the lexicon of world languages. Humor, slang, and creative expressions are a testament to that notion. Even social media has given rise to new creative expressions in ways human society has never experienced before with meme culture as one of the most notable examples. Even if NMT was somehow capable of keeping up, it would still need time for the data to accumulate for it to start translating. By that time, new slang would have already popped out.

Unable to Render Specialized and Highly Contextual Translations

What we mean by specialized here is text with highly nuanced terminology such as the literary field and also texts belonging to critical fields such as the legal, scientific, medical sector. Authors inherently embed their works with highly nuanced expressions and linguistic ‘anomalies’, so much so that there is no identifiable pattern for any MT that can work with since each author has their own voice.

For the legal, and the medical sector, have their own language conventions that although seem formulaic on the surface, the inherently specialized terminologies and the risk factor involved in these fields means no margin of error can be given to MT. There are MT systems used in these sectors but are always paired with a professional legal translator and professional medical translator.

Developing Your Own MT System

Even with the quality issues and other imperfections associated with MT, the demand for machine translation services. According to a report published in Market Watch, “The Global Machine Translation Market was valued at USD 550.46 million in 2019 and is expected to reach USD 1042.46 million by 2025, at a CAGR of 11.23% over the forecast period 2020 - 2025.”.

However, many are looking to develop their own company MT instead of ‘borrowing’ one from an external provider and for good reason. If a translation company is rendering plenty of niche translations in a given year, then configuring their own MT system is the most cost-effective investment as there will be no need to pay for licensing fees to external MT providers.

Many industries have their language conventions and jargon, in regards to internal communication mostly. For example, legalese is perfectly comprehensible to lawyers but downright alien-sounding to those with little legal knowledge. That being said, even businesses and organizations have their own language conventions that veer off from the industry norm. In that case, they would then have to build their very own MT systems, especially if they’re focusing on specific target foreign markets and audiences. 

So out of the 3 listed earlier, which one should you choose? It’s most likely SMT due to its popularity and how much support it gets. There are who have gone for a Hybrid MT by combining SMT and RBMT but that’s probably too intimidating for first-timers. If you want to make the big leap right from the start, then, by all means, go NMT if it meets your company’s objectives. 

Mind you that investing and training any MT system does come at a price and will take time. It’ll take time for glossaries and translation memories to develop, provided that the data used to feed the system is of standard. For a translation company, that usually isn’t a problem as in tandem with open-source parallel text corpora are the translation company’s own document archives.

Can You Choose MT Over a Translation Company?

Back then, instant language translation belonged to the category of futuristic science fiction gadgets. In fact, it still is today albeit we’ve heightened our standards. What we dream of now is instant voice interpretation. Specifically, being able to conduct a seamless multilingual conversation with anyone without the awkward pauses. But let’s get back to reality now. It’s hard not to be impressed with the abilities of MT today since we can easily witness it from our smartphones.

Even so, there are plenty of flaws associated with MT as discussed earlier that’s actually hindering it from developing serious widespread adoption. Be that as it may, MT as it now nevertheless has its own perks. Although one shouldn’t rely too much on MT at certain thresholds, doesn’t mean that you shouldn’t use it at all at specific situations. Here are some reasons why.

Cost

There are plenty of translation apps out there such as Google Translate as you might know already. All of them are free with the exception of premium access subscription payments to unlock more features. There are plenty of free translation plugins as well for website developers. Keep in mind that we’re talking about generic translators here and not the specialized MT systems from external providers that have licensing fees.

Speed and Convenience

At specific situations, some are just looking to have translation at the very moment they want it. Whether you’re a language student or a traveling businessperson, MT is your answer. It’s free and they can get results the moment they click the translate button. Even if it’s not 100% accurate, it at least gives them an implied meaning behind the translation.

For Generic, Repetitive, and Well-Resourced Languages

*Consider this pointer at your own risk*. One can certainly find MT if they have non-contextual and predictable text at hand such as simple and formulaic phrases. What you decide to do with it is all on you whether you use it only as a reference or actually employ it in a professional setting. That being said, the most quality translations you can get are from well-resourced such as Spanish, German, French, etc. If you tried translating, even a simple phrase from English to Chinese, you’ll unlikely get a similarly accurate translation since English and Chinese have vastly different language rules and an unrelated linguistic history.

A Note on Translation Quality in the Context of the Coronavirus Pandemic

Despite the vast improvements to MT, quality is still a significant issue and as you’re aware, human translators are there to guarantee that. However, in no situation is quality ever more necessary than in global communication in crisis as made evident by the current coronavirus pandemic, specifically in the form of medical translation. Medical translation is a highly specialized niche in translation and critical one too wherein the slightest mistranslation would lead to potentially unfortunate and even fatal consequences.

Medical translation must be provided by specialized medical translators who have complete mastery over their language pair (Ex. English to Spanish, Spanish to English) and extensive familiarity with medical terminology, medical practices, and code of ethics. They must undergo additional lengthy training before they can be classified as certified medical translators. That being said, are MT systems out of the picture?

There are MT systems that translate medical documents and medical research, but it must be under constant supervision from a certified medical translator. Connecting it to today’s crisis, there hasn’t been a recent time in history where a speedy translation of medical research has been more important than ever. Medical scientists all over the world are working together to understand the COVID-19 virus for them to come up with viable treatments and eventually, a vaccine. With that in mind, medical translation is the only bridge that’s making this level of coordination between medical scientists around the world possible.

Final Takeaway

Will there be a future where MT would be so advanced and almost human-like that professional translators would be an endangered species? If you were to judge by the pace of development of MT in such a short period, it would not be that unreasonable to believe in a future like that. However, let’s not put too much thought into it as it doesn’t pay attention enough to what is demanded from translation in the first place.

It’s apparent now that MT is good at servicing the translation speed and optimization needs, but as for quality, much of it belongs to the hands, or should I say the mind of a professional translator. That union would likely last for the next few decades. But let’s not hold ourselves to that prediction. Perhaps a game-changing MT feature is just a few years away or if our prediction holds true decades. But still, that’s considering our standards on translations, particularly on quality and human-ness, haven’t changed.

Author Bio:

Laurence Ian Sumando is a freelance writer penning pieces on business, marketing, languages, and culture.

The Changing Legal Technology Landscape

We have been witnessing a dramatic, largely digitally-driven business transformation, affecting many industries over the last decade. The term most often used to describe this phenomenon is “digital transformation,” even though non-digital business structural changes most often accompany it. 

Digitization and datafication are key elements in this kind of transformation. We have seen the impact of this phenomenon most clearly in the retail industry, where giants of yesteryear like Sears and Borders have fallen to more digitally agile contenders like Amazon, who have changed the retail landscape fundamentally. The impact of digital transformation on the legal profession and function has been less dramatic, but after spending a week at LegalWeek20 in NYC in February, it is clear that change is coming to the enterprise-focused legal profession as well.

The emerging changes in the Legal Industry can be characterized along the following five dimensions:

• The changing relationship between Corporate General Counsel and outside counsel
• The changing legal services model
• The growing impact of the data deluge
• The evolving legal technology landscape
• The increasing importance of data security and privacy

The growing tension and disconnect between corporate general counsel and outside law firms

Experts suggest that the disruptive changes in the legal industry began after the 2008 downturn when companies started demanding more from their outside counsel. Today’s law firms find themselves under greater pressure from clients who demand value, efficiency, and transparency in a way that was uncommon ten years ago. The past decade has seen General Counsels (GCs), demanding more for less, but it has also seen a growing awareness that return on investment (ROI) is more important than just the cost.

The shift that began a decade ago, also began the gradual death of the traditional approach to legal billing, the venerated billable hour. Previously, outside counsel time was literally equivalent to money. Lawyers had little incentive to be more efficient and saw no reason to spend non-billable time exploring and deploying new technologies to make themselves more efficient. This traditional approach is changing now, as firms can no longer rely solely on their legal expertise; today, they must increasingly focus on how they deliver that expertise, which calls for increased use of technology and benefits early adopters of disruptive technologies. Legal services have been a buyers’ market for the past decade and corporate law departments now like to see clearly defined value and efficiency.

All this is happening against a backdrop of changing buyer behaviors accelerated by rapid globalization across the whole professional services market. The corporate legal department has historically often been viewed as “deal killers,” but the modern legal department is now often a much more engaged internal business partner in emerging corporate initiatives. Modern legal departments have increasingly shifted their approach to manage the specific changes created by digitalization — today, corporate legal counsel engages with more stakeholders, interacts with more speed and iteration, and are accustomed to the increased technical and collaborative nature of digital work, in addition to handling new information-related risks. The increasingly technologically aware workforce is upping its expectations in terms of the use of technology and effective, rapid communication between service providers and clients.

Technology is changing the general counsel’s role, and law firms need to react to remain competitive. The time has come to embrace emerging technologies that provide clients with efficient solutions to manage and service their current and future needs. Client expectations are changing, technology is having an increasing impact, and new, low-cost legal service competitors are emerging to take a slice of the market. What was already a buyer’s market is becoming more so, with increasingly powerful in-house legal departments stoking up the market for alternative legal service providers (ALSPs).

Some GCs and consultants have even developed tests to measure their outside law firms on how efficiently they perform with commonly used productivity tools and measure competence with widely used technology. These tests reward efficiency, which goes against the yardstick that old-style lawyers have traditionally used to value their work: time. Firms whose working cultures do not evolve to service current market needs efficiently, are likely an endangered species. Law firms need to deliver better quality service, and they need to do it cheaper and faster, which demands more automation and competence with technology. Clients are now much less tolerant of old-style lawyers who resist or refuse to use technology that enables expedited and efficient work production.

In his book “The End of Lawyers?” author and legal tech expert Richard Susskind writes: “It is not easy to convince a group of millionaires [Old Partners at Law Firms]... that their business model [the billable hour] is wrong.”

Law departments are now at what Judith Flournoy, CIO at international law firm Kelley Drye & Warren terms “an inflection point,” where they are likely to have to accelerate their uptake of technological innovations to stay competitive. Competence with analytics, collaboration and office productivity software is increasingly a base requirement for the client today.

The changing legal services market

As the needs of GCs change, we see corporate law departments are in-sourcing more legal work, using more tools and technology that reduces the need for outside counsel, and are using more boutique law firms and quasi legal-service providers (Alternate Legal Service Providers - ALSP).

The traditional structure of partners effectively running the business, with some carefully supervised and limited support services, is outdated today. The General Counsel today can not only shift to another law firm, but could also work with small specialist boutique law firms, and global accounting firms who are increasing their involvement with legal services. The smaller firms tend to be much more innovative, specialized, tech-savvy, and run leaner practices enabled by technology; and thus are often more competitive than large firms. The growing importance and practice of technology-enabled collaboration allow these new service providers to deliver much more integrated, efficient services resulting in deeper client relationships.

The 2017 Litera Report on the State of the Legal Market states: “The potential impact of the Big Four accounting firms on the future market for law firm services cannot be overstated [for firms in jurisdictions where alternative business structures are permitted]. As the ALSP market evolves, the Big Four are likely to play an ever-expanding role.” The Alternative Legal Service Providers market revenue grew from $8.4 billion in 2015 to about $10.7 billion in 2017 and continues to grow rapidly.

ALSPs perform many of the tasks traditionally done by law firms, with the top five tasks identified in a Thomson Reuters survey as:

• Litigation and Investigation Support
• Legal Research
• Document Review
• eDiscovery, and
• Regulatory Risk and Compliance

Ron Friedmann, a partner at Fireman & Company, a legal industry-focused management consulting firm, believes firms need to leverage an ecosystem of players. He says many of the future lawyers will not be lawyers at all. According to Friedmann, “In ten to fifteen years, law firms will be a much smaller share of the total legal market.”

 The ongoing data explosion

The volume and complexity of data have always been a part of the landscape in the legal industry. What is changing is the deluge of data is coming at ever-increasing speeds, increasing variety, and formats, and is also increasingly global and multilingual. The impact of this data explosion is significant, and most legal teams will admit this increase in content is a major challenge facing the legal profession today.

In eDiscovery settings, this also means that the information triage process is more challenging and requires much more automation to handle data volume and variety and increase the capability to deal with much more multilingual data.

The modern enterprise is now much more rapidly and naturally global, and thus the modern legal department and outside counsel need to be able to process content and information-flows in multiple languages regularly. The variety and volumes of multilingual content that legal professionals need to process and monitor can include any of the following:

• International contract negotiations and disputes
• Patent-infringement litigation
• Human Resource communications in global enterprises
• Customer communications
• GDPR compliance-related monitoring and analysis
• Cross-border regulatory compliance monitoring
• FCPA compliance monitoring

The volumes of multilingual content can vary greatly, from very large volumes that might involve terabytes of documents in litigation related eDiscovery, to specialized monitoring of customer communications to ensure regulatory compliance, to smaller volumes of sensitive communications with global employees. Multilingual issues are especially present in cross-border partnerships and business dealings, which are now increasingly common across many industries. Being able to process and analyze large volumes of multilingual data is becoming an increasingly more important requirement.

 The emerging legal technology landscape

Law firms can help general counsel drive efficiencies in business decisions by working together to determine what technology is most beneficial. Firms need to start adopting a collaborative teamwork approach not only with general counsel but also by cooperating with alternative service providers and the Big Four as collaborative partners. The general counsel is also looking for outside counsel to adopt a more client-centric model.

As automation penetrates more deeply into legal practice, we see that the role of technology grows in scope and breadth. Tools can range from a variety of analytics and collaboration tools to structured document management tools, end-to-end litigation, and eDiscovery platforms. More recently, comprehensive information governance tools are emerging to handle the increasing datafication of the modern enterprise, and manage the growing compliance risks involved in conducting business with an increasing digital footprint.

Rather than simply upgrading existing technologies, the true transformation only comes when law firms adopt a robust IT strategy that overhauls their services completely. Automation also only makes sense if it delivers on providing high-quality work more efficiently and delivers predictable value to the client.

“After all, if you’re paying for a service and one supplier, says, ‘that will take two weeks, and we’ll charge you by the hour,’ and another says, ‘that will take us two days, and we’ll charge a fixed fee’—which would you choose?”

Legaltech, notes Richard Tromans, founder of Tromans Consulting, is a “very wide spectrum.” At one end, there is document assembly and robotic process automation, taking the grind out of standard, repetitive work while reducing the time taken to perform tasks, saving costs, removing errors, and improving compliance. This kind of automation falls into the category of optimization. At the other end is natural language processing, artificial intelligence, and virtual assistants, which offer the possibility of really revolutionizing the future of legal services — and opens the door to the prospect of robot lawyers.”

As the volumes of data climb, tools that help lawyers to extract relevance and identify core patterns that become increasingly important. The legal technology community needs to move beyond making vague claims of being AI-based, to showing clearly how machine learning and data-driven algorithms can assist in delivering higher value to an expanding variety of legal tasks and processes.

The increasing importance of data security and privacy

Data security involves both preventing malicious attacks and limiting accidental data loss. However, the distributed nature of technology, enhanced by cloud services, creates vulnerability with employees increasingly working from remote locations, making it harder to secure data.

As DLA Piper partner—and former US Department of Justice cybercrime coordinator—Ed McAndrew observed, “The best evidence is now held in mobile devices and the apps, social networks, and cloud services we utilize with those devices. Any investigator or litigator who ignores that evidence may be committing malpractice in many instances.”

Recent surveys by Gartner suggest that legal leaders have to start investing in digital skills and capabilities, reflecting the evolving role of the legal department as a strategic business partner. “How legal departments build capabilities to govern risk within digital initiatives matters more than the legal advice they provide” says Christina Hertzler, Practice Vice President, Gartner.

Striking a reasonable balance between security and convenience is a challenge faced by all law firms. As organizations change the way they operate, generate revenue, and create value for their customers, new compliance risks are emerging — presenting a challenge to compliance oversight, which must identify, assess, and mitigate risks like those tied to fundamentally new technologies (e.g., artificial intelligence) and processes.

GDPR, CCPA, and other privacy protection regulations will present special challenges for the modern enterprise. Thus, while digital transformation initiatives require active data harvesting to enable better personalization, this data acquisition effort also needs to respect the privacy rights of consumers and customers who may or may not be aware of the extent of the data collection activities. Debbie Reynolds noted the information governance requirements of these regulations at LegalWeek recently. “The new reality is that navigating the data privacy rights of individuals everywhere will be an operational necessity for businesses to thrive in the digital age,” she said.

Legal professionals will need to play a larger role in managing these new risks, which can be devastating and cost millions in reparations and negative consequences. Increasingly these threats originate in foreign countries and sometimes even with support from foreign governments.

Apart from the compliance risks that clients face, law firms themselves are sought-after targets as repositories of privileged data. Law firms are a top target among hackers because of the extensive high-value client information they possess. Hackers understand that law firms are a “one-stop-shop” for sensitive and proprietary corporate information, merger & acquisitions related data, and emerging intellectual property information.

Lawyers are failing on cybersecurity, according to the American Bar Association Legal Technology Resource Center’s ABA TechReport 2019. “The lack of effort on security has become a major cause for concern in the profession.”

As more rapidly flowing multilingual data becomes the norm in global enterprises, new data security risks emerge as employees start using public machine translation to translate privileged business content. The risk is high because publicly available tools are essentially frictionless and require little “buy-in” from users who don’t understand the data leakage implications as they pass privileged content through these systems. The rapid rate of increase in globalization has resulted in a substantial and ever-growing volume of multilingual information that needs to be translated instantly as a matter of ongoing business practice. Multilingual data will become much more pervasive over the coming future as the forces of globalization march onwards.

However, this situation evolves, it seems clear that robust machine translation solutions will be needed for any enterprise or law firm with global ambitions.

3 Ways You Can Become an ‘Augmented Translator’

This is a guest post by Jonathan Grisot who works as a Senior Language Specialist in SDL’s French Language Office in Paris.

The translators working in the Language Offices at SDL are probably amongst the most tech-savvy pool of translators in the business translation industry. Their proximity to developers of a range of translation technology, ranging from TM (Translation Memory) to TMS (Translation Management Systems), to MT (Machine Translation) and other translation process-related tools gives SDL internal translators a natural advantage. The possibilities and potential for transforming business translation processes and outcomes have gone into high gear in the last four years, as a new management team began an overhaul and rationalization initiative that has already been yielding results. In the coming years, we should expect to see much new innovation coming from these strategic efforts. 

SDL is unique amongst the largest 50 LSPs in having the largest pool of in-house translators.  After the initial wave of euphoria around machine learning and "AI," the world is beginning to realize that the best results are often better accomplished by new models of (wo)man-machine collaboration than by machine alone. As tool developers and linguists begin to engage more intensively and as the dialogue shifts from FUD related factors, to true problem-solving focused collaboration, a new breed of technology and services is emerging.  

I have always felt that the best MT technology will evolve from a productive dialogue between competent translators and open-minded MT technology experts. Superior solutions emerge from process innovation entwined with relevant NLP capabilities and expertise, for the purpose of solving high-value business problems. Long-term experience in solving these problems can provide the insight needed to find optimal process-technology-expertise combinations to address the issues at hand. There are use cases where only an expert human would work and there are use cases where machines alone can handle the sheer scale, volume, and timeliness that is needed. However, in this age of digital transformation where the modern enterprise has content as the core of their digital footprint, the role of properly deployed MT can only grow. This will require a man-machine collaboration that adjusts and adapts to the varying needs and shades of grey that are needed to solve these new kinds of translation challenges.    

This post is a perspective from the translator frontline at SDL and represents an attitude that will produce superior services and technology. SDL has a broad and deep engagement with global enterprises around content management and translation challenges and it is clear that the approach needed for the future will by necessity need to be more varied. The reality before us is nicely captured by research shared in the 2019 United Nations Conference on Trade and Development Digital Economy Report, which shows that global internet protocol (IP) traffic, a proxy for data flows, grew from about 100 gigabytes (GB) per day in 1992 to more than 45,000 GB per second in 2017. By 2022, the figure is expected to stand at 150,700 GB per second.

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The future of professional translation is here. Are you ready? Translation is driving the globalization of communication, but it encompasses more than just translation: linguistic advising, review, proofreading, transcreation, subtitling, language consultancy, linguistic content management... the list goes on. No doubt 2020 and beyond is set to increase opportunities for translators to add value to their clients. But, given the rapidly changing world we now live in, how can translators evolve their own services, becoming 'Augmented Translators'?

Engage with technology

The purpose of technology in translation has always been to help translators deliver and finalize content faster. The days when translators were locked up in a library with a pile of dictionaries and a pencil to produce a translation are long gone. Today, content is processed online, from brochures and web pages to user manuals and market outlooks. Even traditional white papers are no longer exclusively published in paper format. And the list of tools, plug-ins, and technologies available to help translators to finalize and reach audiences continues to grow: translation memories, terminology databases, fragment matches, upLIFT, Neural Machine translation, Autosuggest dictionaries, and more.

Even our corporate language is changing with technology: instead of “engaging” with customers, companies “connect” with customers. Now, for those who are familiar with the technologies offered by our flagship solution, SDL Trados Studio, check out the many assumptions raised about our future by language specialists here.

Even more tech-savvy? Check the other side of the fence and see how content will impact the augmented translators’ environment. Discover SDL Content Assistant, a technology that was considered science-fiction several years ago – but is now very real.

Also, with today’s technology, the help provided to translators does not only come from the tools: now, even content creates itself. Now, it’s up to us, translators, to transform it for our local audience.

Specialize in quality levels, not only in specific industries

Fact: the amount of content to translate has reached incredible levels. While SDL translates hundreds of billions of words every year, this figure remains a drop in an ocean of all translated words. What matters is not the amount to translate. What matters is that the result displayed to your audience meets the quality level expected for such content.

However, billions of words also mean billions of possibilities, and augmented translators are aware of one truth that is the current state of affairs: there is no “standard translation”.

All translations are unique, because clients have unique needs, like their customers. And they also have unique constraints, terminologies, processes, and practices.

With the client’s needs in mind, the augmented translator will adjust their effort and the amount of time required to complete their tasks. And the productivity tools available nowadays are here to help them alleviate the burden: the augmented translator never translates from scratch.

The key factor here is to find the perfect dosage in productivity, the right balance between effort and result. It is important to have a strong understanding of the translation workflow, the tools and assets at your disposal, and your own strengths and skills. This will help assess the quality and thus reduce risks.

In fact, “quality” can only be assessed by a human mind, and this is where the augmented translator and the client can collaborate to set expectations on quality. Because both clients and translators know that a “lack of quality” also means “rework”. And while a “high-quality translation” may be expensive, a “low-quality translation” may cost even more.

Inject culture, and acquire knowledge

Augmented translators will speed up the process of integrating their clients’ requirements to get the quality needed, and that is a truth for all industries. But only if they have adequate assets to help them get started in an augmented world.

An augmented translator will take advantage of the following resources:

• Content reuse from translation memories
• Glossaries to apply preferred terminology
• Style guides to comply with formatting, grammar, and stylistic rules 
• The tone of voice or brand guides to convey the brand’s message 
• Project-specific instructions, like character limitations
• Machine and AI-enabled translation engines to accelerate productivity

All these automated tools and assets are literally “knowledge providers” to the translator, and help non-specialized translators to meet client requests even without even knowing the client. These knowledge providers are useful since all the clients have preferred terms, favorite wordings, and different rules.

Of course, this automation can also be error-prone and full of traps: terms in glossaries that do not take the context into account, incorrect source texts written by non-native speakers, corporate jargon not understandable outside of your client’s professional sphere, and more.

This is where the augmented translator has two strong cards to play: culture and understanding.

Augmented translators will be able to spot errors in the source text, avoid using offensive or restrictive content, use the appropriate language for the target audience, rewrite puns, detect dual meanings, adapt to stylistic rules, and correct erroneous terminology used by translation engines, etc.

The augmented translator walks in the footsteps of the ancient copyists and scribes and embraces the same mission and ambition: connect cultures and content to share a message.

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Jonathan Grisot started as a translator in 2007 and currently holds a position of Senior Language Specialist for SDL in Paris. He is responsible for driving Machine Translation initiatives, managing internal training and quality best practices and is still involved in various translation and transcreation projects. He is also managing the Junior Academy, a local SDL onboarding structure for newly hired SDL translators. Born in Burgundy and raised on the French Riviera, Jonathan considers his detective novels, sci-fi and fantasy books as his numerous children.