All your Burning Questions Answered! How Machine Translation Helps Improve Translation Productivity (Part I)

Part I

We had so many questions during the Q&A in our last webinar session ‘How to Improve Translation Productivity‘ by the KantanMT Professional services team, that we decided to split the answers into two blog posts. So, if you don’t find your questions answered here, check out our blog next week for the remaining answers. 

KantanMT_ComputersInternet today is experiencing what is generally referred to as a ‘content explosion!’ In this fast-paced world, businesses have to strive harder and do more to stay ahead of the game – especially if they are a global business or if they have globalization aspirations. One fool-proof way in which a business can successfully go global is through effective localization. Yet, the huge amount of content available online makes human translation for everything almost impossible. The only viable option then in today’s competitive online environment is through the use of Machine Translation (MT).

On Wednesday 21st October, Tony O’Dowd, Chief Architect of KantanMT.com and Louise Faherty, Technical Project Manager at KantanMT presented a webinar where they showed how Language Service Providers (LSPs)  (as well as enterprises) can improve the translation productivity of the team, manage post-editing effort and easily schedule projects with powerful MT engines. Here is a link to the recording of the webinar on YouTube along with a transcript of the Q&A session.

The answers below are not recorded verbatim and minor edits have been made to make the text more readable.

Question: Do you have clients doing Japanese to English MT? What are the results, and how did you get them? (i.e., do you pre-process the Japanese?)

Answer (Tony O’Dowd): English to Japanese Machine Translation (MT) has indeed always posed a challenge in the MT industry. So is it possible to build a high quality, high fidelity MT system for this language combination? Well, there have been quite a few developments recently to improve the prospect of building effective engines in this language combination. For example, one of the latest changes we made on the KantanMT platform for improving the quality of MT is by using new and improved reordering models to make the translation from English to Japanese and Japanese to English much smoother, so we deliver a higher quality output. In addition to that, higher quality training data sets are now available for this language pair, compared to a couple of years ago, when I had started building English to Japanese engines. Back then it was really challenging. It is still requires some effort to build English to Japanese MT engines, but the fact that there’s more content available in these languages makes it slightly easier for us to build high-quality engines.

We are also developing example-based MT for these engines and it so far this is showing encouraging signs of improving quality for this language pair. However, we have not started deploying this development on the platform yet.

KantanMT note: For more insights into how you can prepare high-quality training data, read these tips shared by Tony O’Dowd, and Selçuk Özcan, co-founder of Transistent Language Automation Services during the webinar ‘Tips for Preparing Training Data for High Quality MT.’

Question: Have you got a webinar recorded or scheduled, where we could see how the system works hands-on?

Answer (Tony O’Dowd): If you go on to the KantanMT website, we have video links on the product features pages. So you can actually watch an explanation video while you are looking at the component.

We work in a very visual environment, and we think videos are a great way of explaining how the platform works. And, if you go on to the website, on the bottom left corner of the page, you will find our YouTube channel, which contains videos on all sorts of topics, including how to build your first enginehow to translate your first document and  how to improve the output of your engines.

If you click on the Resources menu on our site, you can access a number of tutorials that will talk you through the basics of Statistical Machine Translation Systems. In other words, explore the website and you should find what you need.

KantanMT note: Some other useful links for resources are listed below:

Question: Do you provide any Post-Editing recommendations or standards for standardising the PE process? You said translation productivity rose to 8k words per day – this is only PE, correct?

Answer (Tony O’Dowd): I will take the second question first! The 8,000 words per day is the Post-Editing (PE) rate, yes. It is not the raw translation rate. In Machine Translation, everything comes out pretranslated. So this number refers to the Post-Editing effort – like insertions, deletions, substitution of words, and so on that you need to do to get the content to publishable quality.

Louise Faherty: What we recommend to our clients is that when it comes to PE, they should try to use MT. A lot of translators who are new to using MT will try and translate manually, which is a natural tendency, of course. But what we advise our clients is to copy and paste the translation (MT) in the engine and use the MT. The more you use MT and the more you Post-Edit, the better your engine will become.

Tony O’Dowd: I will add something to Louise Faherty ’s comments there. The best example of PE recommendations that I have come across is provided by a group called TAUS. They are at the pivot of educating the industry on how to develop a proficiency in PE.

Subscribe to TAUS YouTube channel here.

Question: What do ‘PPX’ and ‘PEX’ stand for (as abbreviations)?

Answer (Louise Faherty  and Tony O’Dowd): PEX stands for Post-Editing Automation. PEX allows you to take the output of an MT engine and dynamically alter that. When would you need to use PEX? Suppose there is a situation where your engine is repeating the same error over and over again. What you can do in such cases is write a PEX file (developed in the GENTRY programming language). This allows the engine to look for patterns in the output of the engine and to dynamically change that in the output.

For example, one of our French clients did not want to have a space preceding a colon mark in the output of their MT (because this was one of their typographical standards and repeated throughout the content). So we wrote a PEX rule that forced a stylistic change in the output of the engine. This enabled the client to reduce the number of Post-Edits substantially.

PPX stands for Preprocessor automation. You can use PPX files for to normalise or improve the training data. It is based on our GENTRY programming language which is available to all our clients for free.

In short then, PPX is for your training data, while PEX is for the actual raw output of your engine.

For more questions and answers, stay tuned for the next part of this post!

LocWorld28 Berlin – Kindle Voyage up for Grabs from KantanMT

KantanMT LocWorld ExhibitorIt’s that time of year again and the European edition of the LocWorld conference kicks off this week from 3-5 June at the Maritim Hotel in Berlin, Germany. KantanMT are exhibiting at Stand #17.

For anyone unfamiliar with the conference, it is considered The marketplace of the language industry and is hosted by Multilingual magazine and the Localization Institute. Its purpose is to be the place where everyone from the language industry can get together to network, build business relationships and learn from industry peers.

If you haven’t registered already, check out the full program and you will see a great line up of sessions, roundtables and workshops to suit all areas within the localization industry.

The KantanMT team have spent the last couple of weeks preparing for the LocWorld conference and we have some great giveaways planned. As part of the LocWorld Prize draw visitors to the KantanMT Stand at #17 can drop their business cards in the big blue bowl to be in with a chance to win a brand new Kindle Voyage. The Voyage is Amazon’s thinnest Kindle yet and has a high-resolution 300 ppi display and a new adaptive front light.

KantanMT Giveaway

We only have one Kindle Voyage to give away, but fret not, as there is plenty of KantanMT.com branded merchandise up for grabs, including polo-shirts, 4GB USB flash drives and pens.

Tony O’Dowd, KantanMT’s Founder and Chief Architect will be available meet with attendees interested in learning about machine translation. Stop by booth #17, or send an email to info@kantanmt.com to arrange a one-on-one with Tony.

Tony will also be speaking at two sessions:

And, when it’s all over, if anyone has time to see the city for the weekend or even just a few hours on Friday before returning home. Here is a list of the top 10 must see Berlin sights!

  1. Berlin Wall
  2. Holocaust Memorial
  3. The Berlin Zoological Garden
  4. Reichstag
  5. Brandenburg Gate
  6. Museum Island
  7. Berlin Cathedral
  8. Checkpoint Charlie
  9. Humboldt Universitaet
  10. Berliner Fernsehturm (Berlin TV Tower)

For all those walkers, if the weather is good, these sights can be seen via the Sandemans free walking tour, which departs daily from East Berlin at 11 AM and 2 PM at the Brandenburg Gate.

Finally, anyone interested in learning more about KantanMT that has not registered for the conference can contact us to get a FREE guest pass to the exhibition hall for a couple of hours to meet the KantanMT team.

We hope to see you in Berlin!

Translation Quality: How to Deal with It?

KantanMTSelcuk Ozcan, Transistent, KantanMT started the New Year on a high note with the addition of the Turkish Language Service Provider, Transistent to the KantanMT Preferred MT Supplier partner program.

Selçuk Özcan, Transistent’s Co-founder has given KantanMT permission to publish his blog post on Translation Quality. This post was originally published in Dragosfer and the Transistent Blog.

 

 

Literally, the word quality has several meanings, one of them being “a high level of value or excellence” according to Merriam-Webster’s dictionary. How should one deal with this idea of “excellence” when the issue at hand is translation quality? What is required, it looks like, is a more pragmatic and objective answer to the abovementioned question.

This brings us to the question “how could an approach be objective?” Certainly, the issue should be assessed through empirical findings. But how? We are basically in need of an assessment procedure with standardized metrics. Here, we encounter another issue; standardization of translation quality. From now on, we need to associate these concepts with the context itself in order to make them clear.

Image 1 blog Transistent

Monolingual issues

Bilingual issues

As it’s widely known, three sets of factors have an effect on the quality of the translation process in general. Basically, analyzing source text’s monolingual issues, target text’s monolingual issues and bilingual issues defines the quality of the work done. Nevertheless, the procedure should be based on the requirements of the domain, audience and linguistic structure of both languages (source and target); and in each step, this key question should be considered: ‘Does the TT serve to the intended purpose?’

We still have not dealt with the standardization and quality of acceptable TT’s. The concept of “acceptable translation” has always been discussed throughout the history of translation studies. No one is able to precisely explain the requirements. However, a further study on dynamic QA models needs to go into details.There are various QA approaches and models. For most of them, acceptable translation falls into somewhere between bad and good quality, depending on the domain and target audience. The quality level is measured through the translation error rates developed to assess MT outputs (BLEU, F-Measure and TER) and there are four commonly accepted quality levels; bad, acceptable, good and excellent.

The formula is so simple: the TT containing more errors is considered to be worse quality. However, the errors should be correlated with the context and many other factors, such as importance for the client, expectations of the audience and so on. These factors define the errors’ severity as minor, major, and critical. A robust QA model should be based upon accurate error categorization so that reliable results may be obtained.

We tried to briefly describe the concept of QA modeling. Now, let’s see what’s going on in practice. There are three publicly available QA models which inspired many software developers on their QA tool development processes. One of them is LISA (Localization Industry Standards Association) QA Model. The LISA Model is very well known in the localization and translation industry and many company-specific QA models have been derived from it.

The second one is J2450 standard that was generated by SAE (Society for Automotive Engineers) and the last one is EN15038 standard, approved by CEN (Comité Européen de Normalisation) in 2006. All of the above mentioned models are the static QA models. One should create his/her own frameworks in compliance with the demands of the projects. Nowadays, many of the institutes have been working on dynamic QA models (EU Commission and TAUS). These models enable creating different metrics for several translation/localization projects.

About Selçuk Özcan

Selçuk Özcan has more than 5 years’ experience in the language industry and is a co-founder of Transistent Language Automation Services. He holds degrees in Mechanical Engineering and Translation Studies and has a keen interest in linguistics, NLP, language automation procedures, agile management and technology integration. Selçuk is mainly responsible for building high quality production models including Quality Estimation and deploying the ‘train the trainers’ model. He also teaches Computer-aided Translation and Total Quality Management at the Istanbul Yeni Yuzyil University, Translation & Interpreting Department.

Read More about KantanMT’s Partnership with Transistent in the official News Release, or if you are interested in joining the KantanMT Partner Program, contact Louise (info@kantanmt.com) for more details on how to get involved. 

Transistent KantanMT Preferred MT Supplier

 

Moses Use Case: KantanMT.com

Moses Core MT use case KantanMTJanuary 2015 marks the last month of the Moses Core project. The project started three years ago in 2012, as a collaborative effort by its members to improve translation processes and to create a competitive translation environment. Over those three years, the translation and MT landscape has changed significantly. This change and the project’s success is in no small part due to the hard work and diligence of the Moses Core project coordinator; TAUS  and with TAUS’s kind permission, KantanMT is republishing the MT use case for the KantanMT Community.

COMPANY NAME

KantanMT.com is a registered trademark of Xcelerator Machine Translations Ltd.

TIME IN MT BUSINESS

The platform was launched commercially in Q4 2013, however, we have been rigorously testing KantanMT.com in academic and commercial settings since 2012. In the beginning, the product was offered as a free trial to the KantanMT Community, and their feedback was instrumental in shaping and improving the platform to what it is today.

MOSES EXPERIENCE

The Moses technology has improved immensely over the past 12-18 months. Developer documentation and support materials, while initially very basic, have matured into a more structured, comprehensive and helpful resource. Additionally, the management of software distributions has made it easier to work with, understand and deploy. These are key elements in maintaining and supporting any open-source technology and have made Moses a key technology for the localization industry.

MosesCore

WHY MOSES?

The rise of the global economy and the driving demand for multilingual translation created a gap in the market for a sustainable translation method that could automatically scale to accommodate fluctuating translation needs. The KantanMT Development team was able to utilize the open source Moses decoder to develop a cloud-based Statistical Machine Translation (SMT) platform, where clients could build and manage their own customized MT engines without compromizing on the ownership of their data. The flexibility, scalability and security of the Moses toolkit made this possible.

The Moses toolkit offers the most flexibility in implementing an SMT solution for commercial purposes, as it allows the system’s training and decoding process to be modified. This has enabled the KantanMT team to create a high-value product that is dynamic and commercially relevant.

To ensure the product could scale and adapt to user needs the KantanMT team needed a decoder that could be built and managed on the cloud. The Moses system enabled this functionality.

Parallel language data is required to train an SMT engine. This data is an important resource for companies, and current generic SMT engines do not guarantee the security or safeguard the ownership of these assets. In using the Moses decoder, the KantanMT team created a product that could ensure its clients’ data was kept private, and not repurposed or reused in anyway.

Many global companies have large repositories of bilingual data, however, they often do not wish to deploy and maintain their own version of the Moses decoder. The KantanMT Development team was able to develop the sophisticated Moses SMT technology into a package that could be easily accessible to companies wishing to translate their content, and over time achieve localization cost savings.

MT STAFF

The current machine translation development team consists of four people, who maintain the platform and build machine translation engines for clients. Due to significant growth in the company over the past year, KantanMT.com will be hiring more staff over the course of the next few months to build engines for clients.

MT SYSTEM INFRASTRUCTURE

Insource or Outsource Moses/Implementation

Based on research, the demands of the language services industry and enterprise machine translation buyers, KantanMT has implemented and customized the Moses decoder in house to create a robust and commercially viable machine translation product that can scale and adapt to our clients’ needs. The original/base KantanAnalytics™ technology was co-developed with the CNGL Centre for Global Intelligent Content, an academic-industry research Centre based in Dublin City University, Ireland. However, all other KantanMT.com technologies have been developed in house by an in house expert development team.

Number of Engines

As of January 2015, the total number of MT engines built on KantanMT.com by the KantanMT community is 6,777 engines.

Volumes

As of January 2015, the total number of training words uploaded to the platform by the KantanMT Community has surpassed 50 billion, and the number of translated words on the platform is now more than 600 million.

USE SCENARIO

KantanMT preferred MT supplier bmmt
KantanMT.com Preferred MT Supplier

bmmt GmbH is a German language service provider with a strong focus on machine translation. It needed a Machine Translation provider, which would give the bmmt team full control of their Machine Translation training data and MT engine customization process at a low investment point. They also required a system which could correctly handle format-specific tagging and transparent transfer of mark-up information.

In early 2013, bmmt joined the KantanMT Community and began testing different customization processes using client specific training data. The team initially experienced minor problems with their SDLXLIFF files. However, the KantanMT development team were able to quickly solve this problem by restructuring some of its tokenizers.

The company began deploying production engines in mid-2013. These were showing particularly high Quality Evaluation (QE) scores due to the quality of their training data and resulted in a considerable increase in translation productivity. bmmt MT technicians found that domain specificity is a better basis for predictable output than sheer input size.

bmmt is currently using approximately 20 KantanMT engines in production across technical and automotive domains. These production ready engines are experiencing high quality metric scores for each language combination.

MARKET POSITIONING

KantanMT.com is one of the market leaders of cloud-based machine translation services. It provides cloud-based SMT services to major global enterprises and software companies wishing to translate large volumes of data. It works directly with companies to develop and implement a long term machine translation strategy, or it works with a select number of language service providers (preferred MT supplier partner program) to supply MT services to large enterprises.

VIEWS ON CURRENT STATE OF MT

Machine translation is now much more widely accepted in the industry, than it was just a few years ago. Since KantanMT.com entered the market in its testing phase in 2012, we have seen an enormous change in the attitudes and perception of MT in the language community. Access to technology such as smart-phones and tablets in non-English speaking nations has driven the global marketplace, and this in turn has increased the need for on-demand translation services – driving demand for MT services. The MosesCore Project has facilitated this demand with an open source solution that made it possible for smaller companies, and startups like us to compete against bigger MT providers, to solve the problem of language.

“The KantanMT platform sets a new industry benchmark in terms of analytics and development tools used to build and measure the quality of Statistical MT Engines. The KantanMT expert development team has introduced some of the industry’s most exciting and valuable technologies built on the Moses decoder, which are helping language and enterprise clients to translate more efficiently and reduce costs.” KantanMT.com founder and Chief Architect, Tony O’Dowd.

For more information on the Moses Core project or to access the original article, please contact TAUS (moses@taus.net) or to find out more about KantanMT.com contact Louise (info@kantanmt.com).

 

 

Language Industry Interview: KantanMT speaks with Maxim Khalilov, bmmt Technical Lead

Language Industry Interview: KantanMT speaks with Maxim Khalilov, bmmt Technical LeadThis year, both KantanMT and its preferred Machine Translation supplier, bmmt, a progressive Language Service Provider with an MT focus, exhibited side by side at the tekom Trade Fair and tcworld conference in Stuttgart, Germany.

As a member of the KantanMT preferred partner program, bmmt works closely with KantanMT to provide MT services to its clients, which include major players in the automotive industry. KantanMT was able to catch up with Maxim Khalilov, technical lead and ‘MT guru’ to find out more about his take on the industry and what advice he could give to translation buyers planning to invest in MT.

KantanMT: Can you tell me a little about yourself and, how you got involved in the industry?

Maxim Khalilov: It was a long and exciting journey. Many years ago, I graduated from the Technical University in Russia with a major in computer science and economics. After graduating, I worked as a researcher for a couple of years in the sustainable energy field. But, even then I knew I still wanted to come back to IT Industry.

In 2005, I started a PhD at Universitat Politecnica de Catalunya (UPC) with a focus on Statistical Machine Translation, which was a very new topic back then. By 2009, after successfully defending my thesis, I moved to Amsterdam where I worked as a post-doctoral researcher at the University of Amsterdam and later as a RD manager at TAUS.

Since February 2014, I’ve been a team lead at bmmt GmbH, which is a German LSP with strong focus on machine translation.

I think my previous experience helped me to develop a deep understanding of the MT industry from both academic and technical perspectives.  It also gave me a combination of research and management experience in industry and academia, which I am applying by building a successful MT business at bmmt.

KMT: As a successful entrepreneur, what were the three greatest industry challenges you faced this year?

MK: This year has been a challenging one for us from both technical and management perspectives. We started to build an MT infrastructure around MOSES practically from scratch. MOSES was developed by academia and for academic use, and because of this we immediately noticed that many industrial challenges had not yet been addressed by MOSES developers.

The first challenge we faced was that the standard solution does not offer a solid tag processing mechanism – we had to invest into a customization of the MOSES code to make it compatible with what we wanted to achieve.

The second challenge we faced was that many players in the MT market are constantly talking about the lack of reliable, quick and cheap quality evaluation metrics. BLEU-like scores unfortunately are not always applicable for real world projects. Even if they are useful when comparing different iterations of the same engines, they are not useful for cross language or cross client comparison.

Interestingly, the third problem has a psychological nature; Post-Editors are not always happy to post edit MT output for many reasons, including of course the quality of MT. However, in many situations the problem is that MT post-editing requires a different skillset in comparison with ‘normal’ translation and it will take time before translators adopt fully to post editing tasks.

KMT: Do you believe MT has a say in the future, and what is your view on its development in global markets?

MK: Of course, MT will have a big say in the language services future. We can see now that the MT market is expanding quickly as more and more companies are adopting a combination TM-MT-PE framework as their primary localization solution.

“At the same time, users should not forget that MT has its clear niche”

I don’t think a machine will be ever able to translate poetry, for example, but at the same time it does not need to – MT has proved to be more than useful for the translation of technical documentation, marketing material and other content which represents more than 90% of the daily translators load worldwide.

Looking at the near future I see that the integration of MT and other cross language technologies with Big Data technologies will open new horizons for Big Data making it a really global technology.

KMT: How has MT affected or changed your business models?

MK: Our business model is built around MT; it allows us to deliver translations to our customers quicker and cheaper than without MT, while at the same time preserving the same level of quality and guaranteeing data security. We not only position MT as a competitive advantage when it comes to translation, but also as a base technology for future services. My personal belief, which is shared by other bmmt employees is that MT is a key technology that will make our world different – where translation is available on demand, when and where consumers need it, at a fair price and at its expected quality.

KMT: What advice can you give to translation buyers, interested in machine translation?

MK: MT is still a relatively new technology, but at the same time there is already a number of best practices available for new and existing players in the MT market. In my opinion, the four key points for translation buyers to remember when thinking about adopting machine translation are:

  1. Don’t mix it up with TM – While TMs mostly support human translators storing previously translated segments, MT translates complete sentences in an automatic way, the main difference is in these new words and phrases, which are not stored in a TM database.
  2. There is more than one way to use MT – MT is flexible, it can be a productivity tool that enables translators to deliver translations faster with the same quality as in the standard translation framework. Or MT can be used for ‘gisting’ without post-editing at all – something that many translation buyers forget about, but, which can be useful in many business scenarios. A good example of this type of scenario is in the integration of MT into chat widgets for real-time translation.
  3. Don’t worry about quality – Quality Assurance is always included in the translation pipeline and we, like many other LSPs guarantee, a desired level of quality to all translations independently of how the translations were produced.
  4. Think about time and cost – MT enables translation delivery quicker and cheaper than without MT.

A big ‘thank you’ to Maxim for taking time out of his busy schedule to take part in this interview, and we look forward to hearing more from Maxim during the KantanMT/bmmt joint webinar ‘5 Challenges of Scaling Localization Workflows for the 21st Century’ on Thursday November 20th (4pm GMT, 5pm CET and 8am PST).

KantanMT Industry Webinar 5 Challenges of Scaling Localization for the 21st Century_Webinar

Register here for the webinar or to receive a copy of the recording. If you have any questions about the services offered from either bmmt or KantanMT please contact:

Peggy Linder, bmmt (peggy.lindner@bmmt.eu)

Louise Irwin, KantanMT (louisei@kantanmt.com)

5 Reasons to Read the TAUS Review

Earlier this month, TAUS, a well-known industry think tank and resource centre for the language services industry launched its quarterly publication; the TAUS review. The new magazine with a mission is dedicated to;

“Making translation technology more prominent and mainstream throughout the globe to break language barriers and improve worldwide communication.”

KantanMT TAUS Review

KantanMT identified five key reasons that make the review an invaluable asset to any translation and localization professional. It’s thanks to these reasons that KantanMT will distribute the TAUS Review right here on the KantanMTblog.

1. Global Translation Industry news 

TAUS has mobilized writers from across the globe; Africa, Americas, Asia and Europe to discuss different trends and technologies in the language services industry. These articles can become a great reference tool for those interested in how language technologies are advancing. In this issue; Andrew Joscelyne reports from Europe; Brian McConnell gives updates from the Americas; Asian trends are covered by Mike Tian-Jian Jiang and Amlaku Eshetie reports from the southern hemisphere; Africa.

2. Research and Reports 

Recent Research in MT is pretty exciting stuff, those that consider themselves language industry veterans like Luigi Muzii remember a time when machine translation predictions were overestimated. But what was once an unrealistic assumption is now changing as “neural networks and big data” are bringing a new frontier to natural language processing. Luigi Muzii gives an overview of the ‘research perspective’, highlighting current trends in research and linking to some interesting ACL winning papers, which introduce MT decoders that do not need linguistic resources.

3. Unique Insights

TAUS Review offers unique insights into the translation industry by incorporating use cases and perspectives from four different personas; the researcher, the journalist, the translator and the language expert, each one with their own different views and opinions on the importance of global communication and breaking down language barriers. In this issue, Jost Zetzsche, Nicholas Ostler, Lane Greene, and Luigi Muzii share their perspectives.

KantanMT especially enjoyed  Jost Zetzsche’s view of making “machine translation translator-centric” where the translator is at the centre of the MT workflow. One of the examples he lists for making this possible, “dynamic improvements in MT systems” is available to KantanMT clients.

4. Language Technology Community 

The opinions and thoughts that come from each contributor are neatly wrapped in one accessible place, and when coupled with the directory of distributors, events and webinars make a very useful resource for any small business or language technology enthusiast. Keep an eye out for some very interesting post-editing and MT quality webinars planned for November.

5. It’s Free! 

Holding true to the concept of sharing information and making translation technology more prominent and mainstream throughout the globe, the review is available quarterly and completely free for its readers, making it accessible to anyone, anywhere regardless of their budget.

Scroll to the end of the page to find the TAUS review on the KantanMTBlog.

TAUS CEO, Jaap van der Meer talks to KantanMT

The translation industry has experienced a great shift in the past number of years, and not many can say they haven’t been affected. The movement to automate translation processes, driven by a remarkable increase in the demand for accessible multilingual content and price pressures on localization professionals can be seen at every level of the translation industry.

TAUS (Translation Automation User Society), a translation industry ‘think tank’ was founded in 2004 as a result of a roundtable held at the Localization World Conference in Seattle at which a group of some of the biggest IT companies in the world; including Oracle, IBM and CISCO sat to discuss the topic of automation and explore ideas of how to support the movement and those it affected by it.

TAUS CEO, Jaap van der Meer talks to KantanMTJaap van der Meer, Founder and CEO of TAUS talked to KantanMT about the evolution of one of the industry’s most well-known resource centres and the rapidly increasing developments in translation technology. He also shares his opinions and thoughts about the translation profession which he sees as having no escape from this global move to automation.

For Jaap, TAUS began as an ideology; he wanted to “help the world communicate better and create bigger opportunities for the translation sector”. He notes how the translation sector differs from other industries in that most industries have developed shared approaches, best practices and common metrics to support themselves and others working within these industries.” The lack of this he says is something that has created a “huge barrier to efficiency and innovation” in the translation industry, and when we remove these barriers “we create a much bigger opportunity for each individual player in the industry”.

TAUS is synonymous with automated translation, and in particular with machine translation. Yet, while Jaap would suggest that this is only one piece of the puzzle, he does believe that in time “every company that operates internationally will have to start using it.”

Machine translation has experienced incredible growth in recent years, both in terms of technological innovation and wide industry adoption. Indeed, Jaap believes that “the investment that goes into improving MT technology and integrating MT and post-editing into translation workflows will be the one thing that has the biggest effect on the industry” over the next few years. He stresses however that this investment needs to feed an entire ecosystem, because MT is not stand alone. “You can’t just dump a machine translation system into an existing environment. You need to change and innovate the whole environment. There’s a lot of evaluation and metrics involved and widespread training needed.”

Another technology that he sees developing in line with machine translation is speech translation, and the convergence of both technologies. Those attending the TAUS annual conference in Vancouver in October will learn more about this as it is the conference theme. So will TAUS offer similar resources for speech translation as with text translation? Well, Jaap admits that although TAUS always tries to be “ahead of the curve”, the process of building such an extensive repository of speech corpora might be too demanding for an industry body of TAUS’ size. The solution? Jaap says they will need to “collaborate with other industry groups and also at a government level” in order to grow in this area.

So, as TAUS continues to expand its services and move into new areas Jaap’s role begins to grow and diversify. What keeps him driven on his pursuit towards language as a utility? “It’s just because I believe in it, if it were just for business, I’d probably do something else.” A nice thought knowing that that there are people working to progress an industry and ease the path for all stakeholders involved.