Tag: Machine Translation

AI Interpreter Fail at China Summit Sparks Debate about Future of Profession

AI Interpreter Fail at China Summit Sparks Debate about Future of Profession

Tencent’s AI powered translation engine, which was supposed to perform simultaneous transcribing and interpreting at China’s Boao Forum for Asia last week, faltered badly and became the brunt of jokes on social media. It even made headlines on the South China Morning Post, Hong Kong’s main English newspaper – which, incidentally, is owned by Tencent’s key rival Alibaba.

The Boao Forum, held in Hainan Province on April 8-11, 2018, is an annual nonprofit event that was started in 2001. Supported by the region’s governments, its purpose is to further progress and economic integration in Asia by bringing together leaders in politics, business and academia for high-end dialogs and networking.

Tencent is one of the tech giants of China, often dubbed the “B.A.T.” (for Baidu, Alibaba, Tencent; sometimes BATX if one includes Xiaomi). Its most well known products include the instant messenger WeChat as well as microblogging site Sina Weibo. Both are everyday apps used by just about all Chinese citizens as well as other ethnic Chinese around the world.

WeChat in China is pretty much an all-round, full service lifestyle mobile app in its local Chinese version. You could do just about anything in it these days – from buying train and movie tickets to making mutual fund investments to ordering groceries or an hourly maid from the neighbourhood.

In 2017, Tencent rolled out an AI powered translation engine called “Fanyijun”, which literally translates to “Mr. Translate”, since the Chinese character for “jun” is a polite, literary term for a male person.

What went Wrong?

Fanyijun is already in use powering the in-app translator in WeChat as well as available online as a free online service. However, it was supposed to have made a high-profile debut at the Boao Forum together with the Tencents “Zhiling” or literally translated, “Smart Listening” speech recognition engine, showcasing the company’s ability to do real-time transcription and interpreting. In retrospect, it seems the publicity effort has backfired on Tencent.

To be sure, human interpreters were still on hand to do the bulk of the interpreting work during the forum. However, Tencent used its AI engine to power the live translation and broadcast of some of the side conferences to screens next to the stage and for followers of the event within WeChat.

This resulted in many users making screenshots of the embarrassing errors made when the engine frequently went haywire and generated certain words needlessly and repeatedly, as well as getting confused when some speakers spoke in an unstructured manner or used certain terminology wrongly.

Chinese media cited a Tencent’s spokesperson who admitted that their system “did make errors” and “answered a few questions wrongly”. But he also said in their defense that the Boao Forum was a high-level, multi-faceted, multi-speaker, multi-lingual, discussion based event. That and the fact that the environment was sometimes filled with echo and noise, added to the challenges their system faced.

“They still need humans…”

The gloating hit a crescendo when someone circulated this screenshot from a WeChat group composed of freelance interpreters. It was an urgent request for English simultaneous interpreters to do a live webcast later that day for the Boao Forum.

One group member replied, “They still need humans…” Another said, “Don’t they have an interpreter device?” A third sarcastically added, “Where’s the AI?”

Tencent later clarified that this request was meant for engaging interpreters for their professional news team doing live reporting in Beijing, and not for the simultaneous interpreting team located onsite at the Boao Forum.

Tencent reportedly beat other heavyweight contenders such as Sogou and iFlytek to secure this prestigious demo opportunity at the Boao Forum after a 3-month long process. Sogou is the 2nd largest search engine in China, which also provides a free online translator, built in part through leveraging its investment in China startup UTH International, which provides translation data and NMT engines. iFlytek is a listed natural language processing (NLP) company worth about USD 13 billion in market capitalization. Its speech recognition software is reportedly used daily by half a billion Chinese users and it also sells a popular pocket translation device targeted at Chinese tourists going abroad.

But given what went down at the Boao Forum for “Mr. Translator”, Tencent’s competitors are probably seeing their ‘loss’ as a gain now. The social media gloating aside, this incident has sparked off an active online debate on the ‘what and when’ of AI replacing human jobs.

One netizen said on Sina Weibo, “A lot of people who casually say that AI can replace this or that job, are those who do not really understand or know what those jobs entail; translation included.”

However, Sogou news quoted a veteran interpreter who often accompanied government leaders on overseas visits. She said, “As an interpreter for 20 years, I believe AI will replace human translators sooner or later, at least in most day to day translation and the majority of conference interpreting. The former probably in 3-5 years, the latter in 10 years.”

She added that her opinions were informed by the fact that she frequently did translation work for IT companies. As such she was well aware of the speed at which AI and processor chips were advancing at, and hence did not encourage young people to view translation and interpreting as a lifelong career, which she considers to be a sunset industry.

Reference: https://bit.ly/2qGLhxu

SDL and TAUS Integration Offers Brands Industry Benchmarking Framework

SDL and TAUS Integration Offers Brands Industry Benchmarking Framework

SDL, a leader in global content management, translation and digital experience, today announced an integration between SDL Translation Management System (TMS), and the TAUS Dynamic Quality Framework (DQF), a comprehensive set of tools that help brands benchmark the quality, productivity and efficiency of translation projects against industry standards.

The SDL TMS integration with TAUS DQF enables everyone involved in the translation supply chain – from translators, reviewers and managers – to improve the performance of their translation projects by learning from peers and implementing industry best-practice. Teams can also use TAUS’ dynamic tools and models to assess and compare the quality of their translations output – both human and machine – with the industry’s average for errors, fluency and post-editing productivity.

This enables brands to maintain quality – at extreme scale – and eliminate inefficiencies in the way content is created, managed, translated, and delivered to global audiences.

“One marketing campaign alone could involve translating over 50 pieces of content – and that’s just in one language. Imagine the complexity involved in translating content into over a dozen languages?” said Jim Saunders, Chief Product Officer, SDL. “Brands need a robust way to ensure quality when dealing with such high volumes of content. Our ongoing integrations with TAUS DQF tackle this challenge by fostering a knowledge-filled environment that creates improved ways to deliver and translate content.”

“Translating large volumes of content quickly can present enormous quality issues, and businesses are increasingly looking to learn from peers – and implement best-practices that challenge traditional approaches,” said TAUS Director, Jaap van der Meer. “Our development teams have worked closely with SDL to develop an integration that encourages companies not just to maintain high standards, but innovate and grow their business.”

The TAUS DQF offers a comprehensive set of tools, best practices, metrics, reports and data to help the industry set benchmarking standards. Its Quality Dashboard is available as an industry-shared platform, where evaluation and productivity data is presented in a flexible reporting environment. SDL TMS, now integrated within the TAUS DQF, is used by many Fortune 500 companies across most industries.

SDL already provides TAUS-ready packages for enterprise with our other language solutions. Customers of SDL WorldServer benefit from a connector to the TAUS DQF platform, enabling project managers to add and track a project’s productivity on the TAUS Quality Dashboard. Users can access both SDL WorldServer and SDL TMS through their SDL Trados Studio desktop, making it easy to share projects with the TAUS platform.

All SDL’s integrations with TAUS are designed to help centralize and manage a brand’s translation operations, resulting in lower translation costs, higher-quality translations and more efficient translation processes.

Reference: https://bit.ly/2EslqhA

What happened at the TAUS Asia Conference 2018?

What happened at the TAUS Asia Conference 2018?

On 22-23 March, 2018, part of the TAUS team was in Beijing for the TAUS Asia Conference. It was the sixth time that TAUS came to China, but we quickly realized that it should actually be an annual event on our calendar. This was the first TAUS conference ever hosted by a university, namely the Beijing Language and Culture University (BLCU).

 BLCU was established in 1962 and is located in the Haidian District in Beijing. They have bachelor and master programs in 8 languages, but also teach computer science and technology and digital media as well as as a translation and interpretation major. In 2011, the university set up the first localization department in China. A tour through the classrooms impressed us all: high tech equipment identical to that found in the European Parliament is used by the students to practice their human interpretation skills.

Being at this prestigious university in the “Hall of Future Global Translation Talents”, was a perfect fit for TAUS and our plan to have, for the very first timelive automatic interpretation technology (using Microsoft Translator) running throughout the program, with the help of Mark Seligman from Spoken Translation. We are finding ourselves now at a crossroads with the rapid revolution of Neural MT and Artificial Intelligence and realize what a huge impact technology will have on the future of the translator profession. In addition to the live automatic interpretation, four students of BLCU provided live interpretation from the professional interpretation booths and via devices handed out to the attendees at the university. They confessed to being a bit nervous when they realized they were ‘competing’ with the live automatic interpretation from Microsoft.

At the end of the conference we invited the four interpreters and the automatic interpretation leader Mark Seligman on stage to evaluate the different interpretation methods and how they competed or interacted with each other. Before the conference, one of the students, Zhu Qiankun, noted the news from Microsoft that their translation quality is at human parity when compared to professional human translations and also find that it significantly exceeds the quality of crowd-sourced non-professional translations. He found that this declaration is somewhat unreasonable and irrational and wrote an essay about it still before he knew he was going to be functioning as a human interpreter at the TAUS Asia Conference. After teaming up with the machine to interpret the presentations at the conference he wrote another essay with his findings and although his overall view on machines taking over the human jobs did not change he also found that there were some advantages of human and machine working together, namely seeing the live translation transcription projected as a large image on the screen helped them interpret faster and more accurately. He also noted that numbers, names and dates are translated better by the machine than by the human interpreter. The live automatic interpretation phenomenon will be repeated at the upcoming TAUS Executive Forum in Tokyo (on 16 and 17 May).

The conference kicked off with a keynote address from Francis Tsang, President of China at LinkedIn. Francis provided deep insights into the Chinese market, with lots of facts and figures about the workforce and trends. It was a perfect start to two-days of brainstorming and knowledge sharing at this prestigious venue. This was followed by a CEO conversation, starring Marcus Casal from Lionbridge, Henry He from TransN and Adolfo Hernandez from SDL. TransN is the largest translation service provider in China and Henry He provided some great insights into the Chinese market. We quickly figured out that some of the recent trends in the western part of the world, such as blockchain technology, are also very much on the minds of people in China. Francis’s and Henry’s speeches confirmed many of the things that TAUS had predicted in the Nunc Est Tempus eBook that came out last December (see chapter: China’s Turn).

Over the next 48 hours we saw innovative presentations from many Chinese companies as well as western companies. For example: Alibaba presented their work with AI and cross border e-commerce, Niutrans showed their latest developments in MT technology, TalkingChina showcased their advances in boutique translation, and Johnson and Johnson gave a crash course on the challenges of pharmaceutical translation with a focus on China. In the Game Changers Innovation Contest we saw nine innovative technologies, ideas or perspectives. The most original idea came from Tianqi Zhang at the Universitat Autonoma de Barcelona, who showed how machine translation can advance the way football is reported all over the world. It’s no surprise that her innovative and unique research was voted the winner of the Game Changers Innovation Contest Beijing 2018.

With over 160 attendees, the TAUS Asia Conference Beijing 2018 was our biggest conference to date. As always, the participants comprised a good balance between buyers and providers. And since we were at the university, we also had some great representatives of the academic world.

The last session of the conference was focused on talents – bringing together the academic and the business world. Alex Han (professor at BLCU and TAUS representative) gave a presentation about what he thought would be ‘the future translator’. Skills and requirements of translators are changing, and Alex is taking the lead in adapting the study programs to meeting these changing needs. Frans de Laet, a guest professor at BLCU, presented his ideas about the humanization of machines in relation to translator jobs. You’ll see a thorough report of this session as well as the others in the upcoming Keynotes eBook coming out in April.

I think it’s safe to say that the TAUS Asia Conference 2018 was a great success. Lots of new perspectives and ideas were shared and brainstormed among the speakers and attendees, and new connections were made and social networks enriched. We are looking forward to coming back to China again soon!

Reference: https://bit.ly/2qdWZzq

Machine Translation and Compliance

Machine Translation and Compliance

 

Compliance management is no simple task in today’s world. The sheer volume of data involved is intimidating enough. But when that data is in multiple languages, you have an additional layer of complexity to manage as well as another significant expense to budget for.

Machine translation is no replacement for expert human translators. But it can help solve some of the compliance problems multicultural organizations face.

Internal Compliance Monitoring

Ideally, organizations should aspire to catch (and end) compliance issues as early as possible. Firing employees is an expense in and of itself, and if you address these issues quickly you can often solve the problem with education rather than termination. Meanwhile, whether the behavior in question is illegal, unethical or just plain risky, the sooner you put a stop to it, the less likely you are to get stuck with expensive fines.

Is your organization monitoring employee communication to identify concerning behavior? Machine translation makes it possible to understand, analyze, and review large amounts of archived data in foreign languages, so you can stop problems before they start.

eDiscovery Compliance

Businesses today generate vast amounts of electronic documents and communications. That makes eDiscovery like looking for needles in a haystack, sifting through tons of irrelevant information to find materials that are relevant to the case. And of course, there are penalties for not identifying and producing all of the necessary documents in a timely manner.

The most workable solution is appropriately-deployed machine translation followed by review and post editing from human experts, when required. Machine translation is not a substitute for human translators. That said, in large cross-border cases, machine translation can be used to produce documents for opposing counsel, and then human translators can translate only those documents that seem relevant. Machine translation can also help your team identify and classify large numbers of documents for review.

Data security

Using machine translation when applicable can also improve data security, as long the platform used is secure. (Note: That means free platforms are strictly off limits!) No matter how careful your employees are, each person who accesses a document creates a new security risk. Machine translation can reduce the number of people who need that access to reduce security vulnerabilities.

Machine Translation and Compliance Budgets

As the cost of compliance goes up, so does the pressure for businesses to make their compliance procedures more efficient. Machine translation can help optimize your compliance budget by only using human translators when necessary.

When Machine Translation is a Compliance Nightmare

When wielded wisely, machine translation can be a powerful weapon in your compliance arsenal. But it can also be risky. For instance, if individuals in your organization rely on free online translation services, your data security could be at risk.

Last year, employees at Norway’s Statoil discovered that sensitive data translated using Translate.com’s free MT tool was available to the public via a simple Google search.

Though the quality of machine translation has improved by leaps and bounds during the past few years, it’s still not a substitute for human translators when clear and accurate translations are required. If inaccuracies make your translations misleading or incomprehensible, that’s a compliance risk, too.

 

Reference: https://goo.gl/krFhns

How to Cut Localization Costs with Translation Technology

How to Cut Localization Costs with Translation Technology

What is translation technology?

Translation technologies are sets of software tools designed to process translation materials and help linguists in their everyday tasks. They are divided in three main subcategories:

Machine Translation (MT)

Translation tasks are performed by machines (computers) either on the basis of statistical models (MT engines execute translation tasks on the basis of accumulated translated materials) or neural models (MT engines are based on artificial intelligence). The computer-translated output is edited by professional human linguists through the process of postediting that may be more or less demanding depending on language combinations and the complexity of materials, as well as the volume of content.

Computer-Aided Translation (CAT)

Computer-aided or computer-assisted translation is performed by professional human translators who use specific CAT or productivity software tools to optimize their process and increase their output.

Providing a perfect combination of technological advantages and human expertise, CAT software packages are the staple tools of the language industry. CAT tools are essentially advanced text editors that break the source content into segments, and split the screen into source and target fields which in and of itself makes the translator’s job easier. However, they also include an array of advanced features that enable the optimization of the translation/localization process, enhance the quality of output and save time and resources. For this reason, they are also called productivity tools.

Figure 1 – CAT software in use

The most important features of productivity tools include:

  • Translation Asset Management
  • Advanced grammar and spell checkers
  • Advanced source and target text search
  • Concordance search.

Standard CAT tools include Across Language ServerSDL Trados StudioSDL GroupShare, SDL PassolomemoQMemsource CloudWordfastTranslation Workspace and others, and they come both in forms of installed software and cloud solutions.

Quality Assurance (QA)

Quality assurance tools are used for various quality control checks during and after the translation/localization process. These tools use sophisticated algorithms to check spelling, consistency, general and project-specific style, code and layout integrity and more.

All productivity tools have built-in QA features, but there are also dedicated quality assurance tools such as Xbench and Verifika QA.

What is a translation asset?

We all know that information has value and the same holds true for translated information. This is why previously translated/localized and edited textual elements in a specific language pair are regarded as translation assets in the language industry – once translated/localized and approved, textual elements do not need to be translated again and no additional resources are spent. These elements that are created, managed and used with productivity tools include:

Translation Memories (TM)

Translation memories are segmented databases containing previously translated elements in a specific language pair that can be reused and recycled in further projects. Productivity software calculates the percentage of similarity between the new content for translation/localization and the existing segments that were previously translated, edited and proofread, and the linguist team is able to access this information, use it and adapt it where necessary. This percentage has a direct impact on costs associated with a translation/localization project and the time required for project completion, as the matching segments cost less and require less time for processing.

Figure 2 – Translation memory in use (aligned sample from English to German)

Translation memories are usually developed during the initial stages of a translation/localization project and they grow over time, progressively cutting localization costs and reducing the time required for project completion. However, translation memories require regular maintenance, i.e. cleaning for this very reason, as the original content may change and new terminology may be adopted.

In case when an approved translation of a document exists, but it was performed without productivity tools, translation memories can be produced through the process of alignment:

Figure 3 – Document alignment example

Source and target documents are broken into segments that are subsequently matched to produce a TM file that can be used for a project.

Termbases (TB)

Termbases or terminology bases (TB) are databases containing translations of specific terms in a specific language pair that provide assistance to the linguist team and assure lexical consistency throughout projects.

Termbases can be developed before the project, when specific terminology translations have been confirmed by all stakeholders (client, content producer, linguist), or during the project, as the terms are defined. They are particularly useful in the localization of medical devices, technical materials and software.

Glossaries

Unlike termbases, glossaries are monolingual documents explaining specific terminology in either source or target language. They provide further context to linguists and can be used for the development of terminology bases.

Benefits of Translation Technology

The primary purpose of all translation technology is the optimization and unification of the translation/localization process, as well as providing the technological infrastructure that facilitates work and full utilization of the expertise of professional human translators.

As we have already seen, translation memories, once developed, provide immediate price reduction (that varies depending on the source materials and the amount of matching segments, but may run up to 20% in the initial stages and it may only grow over time), but the long-term, more subtle benefits of the smart integration of translation technology are the ones that really make a difference and they include:

Human Knowledge with Digital Infrastructure

While it has a limited application, machine translation still does not yield satisfactory results that can be used for commercial purposes. All machine translations need to be postedited by professional linguists and this process is known to take more time and resources instead of less.

On the other hand, translation performed in productivity tools is performed by people, translation assets are checked and approved by people, specific terminology is developed in collaboration with the client, content producers, marketing managers, subject-field experts and all other stakeholders, eventually providing a perfect combination of human expertise, feel and creativity, and technological solutions.

Time Saving

Professional human linguists are able to produce more in less time. Productivity software, TMs, TBs and glossaries all reduce the valuable hours of research and translation, and enable linguists to perform their tasks in a timely manner, with technological infrastructure acting as a stylistic and lexical guide.

This eventually enables the timely release of a localized product/service, with all the necessary quality checks performed.

Consistent Quality Control

The use of translation technology itself represents real-time quality control, as linguists rely on previously proofread and quality-checked elements, and maintain the established style, terminology and quality used in previous translations.

Brand Message Consistency

Translation assets enable the consistent use of a particular tonestyle and intent of the brand in all translation/localization projects. This means that the specific features of a corporate message for a particular market/target group will remain intact even if the linguist team changes on future projects.

Code / Layout Integrity Preservation

Translation technology enables the preservation of features of the original content across translated/localized versions, regardless of whether the materials are intended for printing or online publishing.

Different solutions are developed for different purposes. For example, advanced cloud-based solutions for the localization of WordPress-powered websites enable full preservation of codes and other technical elements, save a lot of time and effort in advance and optimize complex multilingual localization projects.

Wrap-up

In a larger scheme of things, all these benefits eventually spell long-term cost/time savings and a leaner translation/localization process due to their preventive functions that, in addition to direct price reduction, provide consistencyquality control and preservation of the integrity of source materials.

Reference: https://goo.gl/r5kmCJ

Adaptive MT – Trados 2017 New Feature

Adaptive MT – Trados 2017 New Feature


SDL Trados Studio 2017 includes new generation of machine translation.

How does it work?

It allows users to adapt SDL Language Cloud machine translation with their own preferred style. There is a free plan and it offers these features:

  • 400,000 machine translated characters per month.
  • only access to the baseline engines, so this means no industry or vertically trained engines.
  • 5 termbases, or dictionaries, which can be used to “force” the engine to use the translation you want for certain words/phrases.
  • 1 Adaptive engine.
  • Translator… this is basically a similar feature to FreeTranslation.com except it’s personalized with your Engine(s) and your termbases.

How does it help?

  • Faster translation with smarter MT suggestions.
  • Easy to use and get started.
  • Completely secure – no data is collected or shared.
  • Unique MT output, personal to you.
  • Access directly within Studio 2017.
  • No translation memory needed to train the MT.
  • Automatic, real time learning – no pre-training required.

What are the available language pairs?

Uptill now, Adaptive MT is available in these language pairs:

English <-> French
English <-> German
English <-> Italian
English <-> Spanish
English <-> Dutch
English <-> Portuguese
English <-> Japanese
English <-> Chinese

For reference: https://www.sdltrados.com/products/trados-studio/adaptivemt/

The Translation Industry in 2022

The Translation Industry in 2022

In this report, TAUS shares predictions for the future of the translation industry in line with their expectation that automation will accelerate in the translation sector during the coming 5 years. The anticipated changes will inevitably bring along various challenges and opportunities all of which are explained thoroughly in the Translation Industry in 2022 Report.

The report explains the following 6 drivers of change

1. Machine Learning

Machine learning (ML) was introduced in the 1950s as a subset of artificial intelligence (AI), to have programs feed on data, recognize patterns in it, and draw inferences from them. 2016 was the year when ML went mainstream, with a lot of applications that were almost unimaginable a few years earlier – image recognition and self-driving cars are just two examples.Computational power and unprecedented advances in deep neural networks will make data-driven technologies astonishingly disruptive. This might be also the case of MT.

As a rule, the growth of machine intelligence represents a threat to many human jobs as people will be replaced by intelligent systems.  The majority of creative jobs is relatively safe while sales jobs could be at risk. The forecast is dubious for technology jobs, but the more senior jobs being relatively secure, while computer programmers and support workers may likely be replaced.  The assumption that jobs requiring manual dexterity, creativity, and social skills are the hardest to computerize is already obsolete: new developments in deep learning are making machines more powerful than anticipated, especially in areas relating to creativity and social interaction. 

In the translation industry – as in other industries – many functions will be affected – whether enhanced, expanded or replaced – by ML.

2. Machine Translation

In the past years NMT has been said to be achieving impressive results, and it is more and more often presented as a replacement for SMT. Advances in artificial neural networks are bringing extremely high expectations, suggesting that NMT could rapidly achieve higher accuracy than SMT. Independent evaluators fnd that NMT translations are more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Better quality MT will mean that a broader range of document types and audiences can be addressed.
NMT will help the further expansion of speech-to-speech (S2S) technologies, now available mostly as English-based monolingual systems. Transforming these into multilingual systems implies many deep and expensive changes. Most S2S technologies are still at an infancy stage and confned to university labs. NMT will help bring speech-enabled devices to the streets.

MT will lead to the ultimate disruption in the translation industry when, only the premium segment of artsy—and possibly life sciences—
translation will remain tradable.

3. Quality Management

Due to the uncertainties intrinsically involved in translation quality assessment, and the fixity of the relevant concepts in the translation community, users seem now willing to accept good enough MT output, especially for large volumes, delivered virtually in real time. For serviceable MT output with no human intervention downstream, TAUS coined the acronym FAUT (Fully Automated Useful Translation) already in 2007. Investing in quality-related decision support tools has become essential to gain translation project insights and beneft from MT.
Applying machine learning to data-driven translation quality assessment will be a disruptive innovation that will call for a major shift in conception and attitude.  Data-driven applications in translation quality assessment will go from document classifiers to style scorers, from comparison tools to automatic and predictive quality assessment, from content sampling to automatic error detection and identification. The data-driven approach to quality will require another major attitude shift.

4. Data

There is a strong need for data scientists/specialists/analysts, but this profile is still absent from the translation industry.

Data has been the fuel of automation, and after entering the automation era at full speed, we are being challenged with many issues.  Translation data is typically metadata: data about translation that can be harvested downstream the closure of a translation project/job/task.  The analysis of translation data can provide a very valuable insight into the translation processes to find the best resource for a job, to decide what to translate and which technology to use for which content. Translation data will be more and more frequently generated by algorithms. More data will come from rating staff and KPIs. All these kinds of data will come from ML applied to translation management platforms, which will get rid of human involvement.

Erroneously, also data covering multilingual text resources is labeled as translation data. In fact, language data specifically consists of translation memories, corpora, and lexicographical and terminological collections. Of course, all these resources have metadata too, which could be exploited. Stakeholders should become more open and massively start sharing their translation metadata to make it the real big data of the translation industry.

There is a strong need for data scientists/specialists/analysts, but this profile is still absent from the translation industry. Translation companies should be looking out for these specialists who can mine and use data for automation. This will most probably lead to a further reduction of the number of translation companies that are able to float and thrive in a more and more competitive market. The challenge for the next few years might be the standardization of translation data in order to shape it and make it convenient for users to derive the maximum benefits from it.

5.  Interoperability

Interoperability is the ability of two different systems to communicate and work together through a common language or interface. While many other industries have flourished thanks to standardization which led to interoperability, automation and innovation, the translation industry has always suffered from a lack of interoperability. This has been costing a fortune for years, both on the client side (in translation
budgets) and on the vendor side (in revenues).
  Things have been changing a lot since 2011, when TAUS published a report on the costs from
lack of interoperability in the translation industry
. Many blame the lack of compliance to interchange format standards as the primary barrier to interoperability, and no one believes any longer that true interoperability in the translation industry can be achieved only through awareness programs, education, and certifications. Interoperability should come from the adoption of standards created by consortia and not from the dominance of a market leader.

The spreading of MT has forced a breakthrough in the interoperability dilemma, starting a wave of innovation and renewed efforts. Most of these efforts have still been focusing on APIs though, as XML has been established for years as the common language, bringing everyone the industry to find its child formats TMX and XLIFF essentially enough.  So far, most of the many APIs made available are meant to simplify the translation business process and reduce translation management and overhead cost. Only a few have been designed to help disintermediation and facilitate access to services.

In this case, we could expect that the most influential buyers of localization and translation services will advance their requests; the technology vendors with the necessary technological and financial resources will fulfill those requests or even introduce their own solutions on the market, just as it happened in the past.

6.  Academy

Translation education is vocational by definition: it prepares people to work in the trade as translators. None of the skills translation students acquire is anything but sophisticated.  Today, many players in the translation industry complain about the lack of good translators, but they seem to ignore that, more than in many other academic fields, translation education follows obsolete models that are still shaped for the 20th century. To make matters worse, the gap between the academic world and the industry is so wide that, when approaching the job market, translation graduates instantly and bitterly realize they don’t know much about the actual work they are supposed to do. They also discover that the world is not interested in their basic skills.

The future may not really need translators, at least not in the old way, as the audience will become even more forgiving for lesser quality of fast-moving content. A highly-automated localization environment will depend on human skills in quality evaluation, content profiling, cultural advisory, data analysis, computational linguistics, and gradually less and less in post-editing; translating plain text will indeed be a long-tail business.

The success of any innovation depends entirely on the people that are going to nurture, develop, and implement it; in times of exponential growth, education is vital to drive adoption and prepare the next generations of workers. Employers should play a part in closing the skills gap with continued professional training. It is never too early to prepare for the future; vast workforce and organizational changes are necessary to upend stale business models and related processes.

For more details, download the full report.

‘Human Parity Achieved’ in MT

‘Human Parity Achieved’ in MT

According to Microsoft’s March 14, 2018 research paper with the full title of “Achieving Human Parity on Automatic Chinese to English News Translation,” a few variations of a new NMT system they developed have achieved “human parity,” i.e. they were considered equal in quality to human translations (the paper defines human quality as “professional human translations on the WMT 2017 Chinese to English news task”).

Microsoft came up with a new human evaluation system to come to this convenient conclusion, but first they had to make sure “human parity” was less nebulous and more well-defined.

Microsoft’s definition for human parity in their research is thus: “If a bilingual human judges the quality of a candidate translation produced by a human to be equivalent to one produced by a machine, then the machine has achieved human parity.”

In mathematical, testable terms, human parity is achieved “if there is no statistically significant difference between human quality scores for a test set of candidate translations from a machine translation system and the scores for the corresponding human translations.”

Microsoft made everything about this new research open source, citing external validation and future research as the reason.

Reference: https://goo.gl/3iFXXG