KantanMT is looking for a Localization Engineer / MT Project Coordinator

Jobs at KantanMTKantanMT is growing! If you are as enthusiastic about MT as we are- we want you! We are looking for someone to join our Professional Services team.

In this team we deal with setting up and testing new engines for our clients and offering an exceptional customer experience. If the description below sounds like you, send your CV and cover letter to louiseq@kantanmt.com

Job description

  • Localization Engineer/MT Project Coordinator

Scope of job

  • The Localization Engineer/MT Project Coordinator provides client support in the setting up and linguistic testing of a MT engine for KantanMT clients.
  • Project deliverables fully tested and certified SMT systems built using the KantanMT platform. This will include data preparation for training, testing, Language Quality Review and deployment of final approved engine into production.
  • Project tasks include evaluation, preparation, compilation, testing, delivery, troubleshooting and back up of the project files.
  • Resources Managed: Ongoing MT Analysis, Translation Memory and Machine Translation platforms.

Position relationships

  • Reports directly to the Professional Services team lead.

Position responsibilities

  • Evaluate and prepare project deliverables (software, help, docs, websites and others) for translation following set quality standards. This process typically includes the use of CAT tools as well as writing instructions for translators
  • Format, test, deliver and back up the project deliverables following set quality standards
  • Completion of projects on time and in line with quality standards
  • Provide daily feedback to the Team Lead on project tasks status
  • Manage Translation Memories and Machine Translation systems
  • Customising, Improving and deploying project Machine Translation engines
  • Methodical analysis of client feedback to provide meaningful improvements to output
  • Support towards colleagues in other departments or linguists on CAT tools

Software applications used

  • Windows platform: MS Office, MS Visual Studio, Alchemy Catalyst, InstallShield, RoboHelp, RoboHTML, HTMLQA, web browsers, Notepad++, Wordfast & related programs (e.g. S-Tagger), SDL Trados Studio, Idiom WorldServer, other CAT tools, KantanMT.com
  • Macintosh platform: ResEdit, Resourcer, Installer VISE, Stuffit InstallerMaker, Appleglot, Powerglot, MPW.
  • Others: VMware, Perl, Python, C++ or C#

Position requirements

  • Computer Degree or other in similar discipline
  • Computer experience in the translation/localization industry
  • A second language is preferable
  • Understanding of localization engineering processes and tools
  • Experience with recent Windows operating systems. Macintosh or Linux operating systems knowledge is a plus
  • Scripting experience is a plus
  • Enthusiastic, organized, flexible and attention to detail
  • Ability to manage stress and collaborate
  • Good communication skills

Interested?

Please send your CV and a cover letter to louiseq@kantanmt.com and tell us why you’re the right candidate for this job.

Cloud Technology Translates into Success for KantanMT

 

Tony O'DowdKantanMT Founder and Chief Architect, Tony O’Dowd was recently featured in one of Ireland’s major national newspapers; The Irish Times.

The author of the news article, Olive Keogh is a business journalist who specialises in writing about innovative Irish enterprises and startups. With Olive’s kind permission, we are republishing the Irish times article.

 

“It’s not widely known at home but Ireland has developed an international reputation for research in statistical machine translation. Trinity, DCU and UL are all recognised worldwide and 120 PhD students have graduated here with skills in the field in the last five years. That’s more than in any other country in Europe,” says Tony O’Dowd the man behind KantanMT, a new scalable, high-speed machine translation system based on the Moses decoder and the Amazon Web Services and Cloud Computing infrastructure.

O’Dowd has spent almost 30 years in the software localization sector with companies such as Lotus Development Corporation and Symantec. Xcelerator, the company behind KantanMT, is O’Dowd’s second start-up, but he was also involved in the formation of FIT, a training organisation set up in 1998 to provide IT skills and training for the long-term unemployed.

Economics of the Cloud

“We are leveraging the Moses MT decoder and multiple streams of research from the Centre for Global Intelligent Content to make statistical machine translation (SMT) technology available to the masses,” he says.

“Traditional SMT systems are slow, expensive to deploy, time-consuming to customise and complex to manage. In short, not for the faint-hearted. I wanted to harness the economics of the cloud to solve these problems. Using hundreds of high-powered cloud-based severs to convert training data into data models also accelerated the process of customisation and the development of SMT engines.”

O’Dowd points out that in addition to the cost factor, traditional SMT solutions can produce translations of dubious quality. By focusing on advanced natural language processes and data processing algorithms, KantanMT also addresses these quality issues.

“Because of the costs involved, SMT tends to be used by large organisations with big budgets and plenty of people available to work on the system. The KantanMT platform removes this expense and complexity and makes it a far more practical and usable tool for businesses both big and small. Our clients can customise, improve and deploy their own engines in a matter of days,” O’Dowd says.

Software Localization

O’Dowd took his first steps as an entrepreneur in 2000 when he set up Alchemy Software Development. It quickly became a leading player in the software localization sector with over 27,000 licences in use worldwide. This success didn’t go unnoticed. The company was sold to the largest privately owned localization service provider, Translations.com, in March 2007.

Prior to setting up Alchemy O’Dowd was technology manager for Symantec Corporation Ireland and responsible for establishing the organisation’s Asian localization hub in Japan. He was also executive vice-president of Corel Corporation and spent three years as a lecturer in Trinity College Dublin teaching microprocessor design and assembly language programming.

O’Dowd began working on the idea for KantanMT in 2011 while on a year “off” to retrain himself on cloud-based technologies. He employed an MBA student to do detailed research into the barriers preventing companies using SMT and says the major leap forward in computing and storage capacity provided by the cloud enabled him to build a platform for SMT systems that would have been inconceivable without it.

Xcelerator recently raised €1.1 million in seed funding from venture capital company Delta Partners and the Enterprise Ireland High Potential Start Up fund. Early versions of KantanMT were given away free to kill competition and grab market share but first revenues (based on a usage pricing model) began flowing this time last year and O’Dowd says it is now profitable. A second round of funding is planned for later this year.

The company currently employs 11 people in its offices in Dublin and Galway, but this is expected to rise to 20-25 by the end of 2015. Its focus is the export market and its biggest customers are independent software vendors from industries such as ecommerce, finance and electronics. The company also provides MT services to the language industry.

School of Hard Knocks

“Starting your first business is definitely daunting as everything is new and you’re travelling down every road for the first time,” O’Dowd says.

“Next time around there is a lot of commonality and because you’ve learned by engaging with the school of hard knocks, you’re better at anticipating the problems and meeting the challenges. You also have a better network of contacts, you’re less frazzled when things don’t go right and you can actually grow the business faster and at a higher level. You also get a better hearing from the funding community as they view you as a safe pair of hands.”

KantanMT is based in the Invent Building at DCU and O’Dowd says the resources and expertise provided by the Invent team were instrumental in getting KantanMT.com off the ground.

“KantanMT.com is the fastest growing SMT platform in the localization industry today. So far over 80.5 billion words have been uploaded to the platform as training data and more than 750 million words have been translated by our clients. When you consider this has all happened in the last nine months, the company is rapidly becoming one of the biggest translation hubs in the market,” O’Dowd says.

Irish Times Business

 The original article was published on Mon, Apr 27, 2015

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Email info@kantanmt.com to learn more about how the KantanMT platform operates, or if you would like to set up a personalised demo with Tony.

Language Industry Interview: A Chat with Deepan Patel from Milengo

Deepan croppedKantanMT caught up with Milengo’s Machine Translation Solutions Architect, Deepan Patel earlier this week for a quick chat about his experience using machine translation. Next Month, Deepan will be joining Tony O’Dowd in a free live webinar, to talk about how Milengo maximized it’s ROI for machine translation.

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

Deepan Patel: To be honest, I sort of fell into the localization industry but I am certainly very glad that I did! I am a Modern Languages graduate from the University of Oxford which provided a very traditional approach to translation, certainly a million miles away from the realities of life in the localization industry.

I moved to Berlin after graduating in late 2008 and within a year I was fortunate enough to be accepted on a trainee program by my current employer Milengo Ltd, a language services provider which was founded in 2005. The first ever project I ever worked on was one that involved the customization of statistical machine translation (SMT) engines for a customer wishing to test the long-term viability of incorporating machine translation and post-editing into their localization operations.

It was a tremendous experience for both myself and Milengo; it was really that initial project that has laid the foundations for the MT-related services that we now offer. The main focus of my work at Milengo relates to testing and deploying customized machine translation and post-editing workflows for clients requiring a completely outsourced MT solution.

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

DP: I believe that having machine translation and post-editing as part of our service spectrum has lent us a significant competitive advantage. This was very apparent in September last year when we were approached by an eCommerce company with quite a formidable challenge: namely, they had 19 days in which to launch a new web shop for Sweden and around  780,000 words that needed to be localized from Danish into Swedish. And of course they had a very tight budget!

Through the experiences that we have gained running large-scale machine translation and post-editing projects over the years, we were able to confidently provide a compelling MT-based workflow solution which fell within our client’s budget and would deliver high-quality translated content before their launch date. When providing their reasons for choosing us as for that project, it was our confidence in stating that we could deliver in time that was the main factor. Without our experience with machine translation, we would not have been able to win that project – it is as simple as that. We were able to deliver high-quality localized content within budget and before the initial deadline request. And now we enjoy regular work from this client, localizing all the updates to their product descriptions across three language pairs.

So in essence, MT has enabled us to win those large-scale projects where customer budgets are limited, turnaround time is crucial but quality expectations are high, that we may not have stood a favourable chance of winning previously.

KMT: How do you use machine translation for your clients?

DP: When answering this question I must take pains to emphasize that our MT service offerings always involve post-editing. For one of our clients within the IT domain, we localize the online help to their software products across five language pairs using customized engines that have been built using their own language assets. The requirement there is to deliver high-quality localized content at a significant cost reduction to a human-only translation model. For this particular customer we have achieved cost savings of between 27 – 40 % depending on the language pair.

For another of our clients within the automotive sector, we have built custom MT systems across 3 language pairs to provide a cost-effective but high-quality localization solution for their huge volume of parts data. The initial challenge presented to us was to localize around 300,000 words of this data within a fairly tight timeframe – though not as challenging as our eCommerce client! We were first able to demonstrate the viability of customized machine translation and post-editing for this type of content via our free Machine Translation and Post-editing (MT-PE) feasibility study, after which point we deployed our workflow solution for their three requested target languages. Again for this customer, we have implemented cost savings of between 25 – 40% when compared to the traditional translation model and are enjoying continued business from them.

The third main scenario where we apply MT-PE is for our eCommerce client that I mentioned in my response to your previous question. They add new products to their web shop on a weekly basis and their very repetitive product descriptions need to be localized as soon as possible, so the content can go “live” on the different language sites. Together with this customer we are now focusing on automating as much of the project process as possible with regard to transfer of content via API connectors and using our customized MT systems as a fully-integrated part of their localization project workflow.

For all of these clients, we have been able to offer tiered-pricing packages based on the premise that the more content that we post-edit and feed back into their MT systems during re-training cycles, the better the system will perform on future projects. Consequently we can offer lower rates for localization at defined intervals. Really it is all about being able to demonstrate the long-term cost-savings possible with a customized MT-PE solution.

KMT: What advice can you give to translation buyers, interested in implementing a machine translation workflow strategy?

DP: Well, firstly I would encourage translation buyers to evaluate whether they have the time, budget  and most importantly the relevant personnel within their organization to develop a custom MT solution, or whether it would make sense to turn to external help in the form of MT tech providers like KantanMT, or LSPs such as Milengo who would additionally be able to provide post-editing solutions as well.

I would also encourage translation buyers to evaluate how MT can be applied in different usage scenarios. For example, it would certainly be worth investigating MT-PE for large volume, highly repetitive content (user manuals, support documentation, catalogue data) where you can achieve significant cost-savings and quicker turnaround without compromise on the language quality (with excellent post-editors of course). Another worthwhile scenario for MT would be if your company produces a lot of short life-cycle or customer support content which needs to be available in the languages of your customers as quickly possible, and where transfer of meaning takes precedence over linguistic quality.

Thirdly I would ask the respective translation buyer to examine the state and volume of any language assets that they can use for customizing MT systems. Do you have enough of a training corpus to build MT systems which produce good quality MT output? Have your language assets been maintained well enough to ensure as much consistency in translation as possible? Remember that an MT system will only ever be as good as the material you use to train it. Again here external help may be useful in terms of applying data cleaning and normalization to the training corpus before you get round to building your MT systems.

Finally, I would always advise prospective translation buyers to consider the wider impact benefits of incorporating MT into their localization practices. The more you make use of your custom MT systems and more post-edited content you incorporate into system re-training cycles, the better your systems will perform. This of course leads to greater productivity benefits and reduced costs for localization. Which in turn means that you should free up more of your budget to turn your attentions towards localizing content that was previously considered too cost-prohibitive.

Maximizing ROI webinar KantanMTThank you Deepan, for taking time out of your busy schedule to take part in this interview, and we look forward to hearing more from you in KantanMT’s upcoming partner webinar. The webinar, Maximizing ROI for Machine Translation will be held on Wed, Mar 11, 2015 3:00 PM – 4:00 PM GMT.

Register for Webinar

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)

KantanMT @tekom Trade Fair and tcworld 2014

The KantanMT team are excited to be exhibiting for the first time at the tekom Trade Fair and tcword conference. This year, the event has found a new home at the International Congress Centre (ICS) Messe Stuttgart. This event, which is the largest of its kind is the biggest market place for technical communication in the world.

Not only will the KantanMT flag be flying high at the largest global TC event. But, KantanMT will also be taking part in sessions, tool presentations and offering personalized demos throughout the conference week. KantanMT are also offering its members a complimentary ticket to the tekom Fair with their registration.

KantanMT tekom trade fair, tcworld
Email us if you still need to get your free ticket (louisei@kantanmt.com)

Session: How does your machine Translation system measure up?

Tony O’Dowd, Founder and Chief Architect will be giving a presentation on evaluating machine translation. The presentation; ‘How does your machine Translation system measure up?’ is for localization professionals and will cover some of the most common yet critical issues for users of machine translation:

  • Measuring performance of Statistical MT
  • Recent advances in MT and data visualization techniques
  • Tracking MT efficiency in the translation process

Find it

What: Session

Where: Room C7.1OG

When: Wednesday 12th November @16:00 – 16:45

 

Joint Tool Presentation – Machine Translation for Translation Buyers: What is available and what is expected!

On the following day, KantanMT will be taking part in a joint tool presentation with German Language Service Provider (LSP) bmmt. Tony O’Dowd and Maxim Khalilov from bmmt will discussmachine translation for translation buyers: what is available and what is expected’. In this presentation, Tony and Maxim will give an overview of the current post-edited MT landscape and discuss with examples the formula for successful MT adoption, as well as what tools are available for global translation buyers. The full tool presentation program is available online on the tekom website.

Find it

What: Tool Presentation

Where: Room C10.1

When: Thursday 13th November @ 11:15 – 12:00

KantanMT_tcw-conference2014_Exhibitor

Personalized Platform Demonstrations

At the KantanMT exhibition booth, the KantanMT team will be giving personalized platform demonstrations that provide an ‘under the bonnet’ look at the cloud-based platform. The booth will be located in Hall C2 at booth A10, right next to bmmt; German LSP and KantanMT preferred partner.

Find it:

What: Personalized Platform Demonstrations

Where: KantanMT exhibition booth Hall C2, booth A10

When: Tuesday 11th – Thursday 13th November

 

Get the most out of the tekom/tcword conference – meet the teams

Large conferences and events can often be overwhelming and it’s easy to lose track of time and get wrapped up in the buzz and excitement of the event. To make sure you get the most from the conference, keep organized and make an appointment to speak with a member of the KantanMT or bmmt team.

KantanMT team – contact Louise Irwin (louisei@kantanmt.com)

Bmmt team – contact Peggy Lindner (peggy.lindner@bmmt.eu)

See you in Stuttgart!

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.

New Trends in MT

communicate-Globally---in-any-language

Machine Translation (MT) has experienced an impressive growth in the rate of adoption over the last two years and today is being used by a growing number of enterprises and Language Service Providers (LSPs). Increasing numbers of MT vendors are now coming to market offering solutions which open MT up to a much wider audience and with a broader range of usage possibilities.

The motivation to use Machine Translation arises from many factors:-
  • Improvements in MT quality and the introduction of SaaS based platforms.
  • Exponential growth in digital content production and a growing customer expectation for information in their own language.
  • An increase in online social community activity and a desire to interact with international users.
  • Increasing competition within global markets and a motivation to provide more value to customer and stakeholders.

 

New Trends in Machine Translation

 

Google and Microsoft are probably the most well know MT providers for the general public, however, when we talk about enterprise users or the Language Industry, different suppliers of MT services and technology take precedence.

Traditionally, enterprise Machine Translation was supplied through high-cost consultancy organisations – with lengthy and complex deployment models and high project costs. Add to this, maintenance and pay-per-word annualised contracts and these costs mount up fast! In this scenario, small to medium-sized enterprises weren’t able to afford MT and so the majority couldn’t embrace MT within their organisations.

The Dawn of a New Era!

So how has Machine Translation changed in the last few years and what direction is the technology moving towards?

Well, as companies become more aware of the cost reductions that MT can deliver, they are beginning to search for sustainable MT solutions which will allow them to control the MT development process, and the integration and deployment process, at a reasonable price. These progressive organisations are searching for a solution that will protect their data and their client’s data, will provide maximum Return on Investment (ROI) and will leverage their existing translation assets and add velocity to their localisation workflows.

Understanding these challenges was the central motivation behind the development of KantanMT.com; a cloud-based platform used to customise, improve and deploy Machine Translation within translation workflows and software applications. KantanMT’s Founder and Chief Architect, Tony O’Dowd, a well-known localization industry entrepreneur became more aware of the language challenges facing international companies and LSPs around 2010 and started to develop an industry solution that would make MT available to a much wider audience.

Automating processes that consultancy companies were doing by hand would maximise efficiencies during the MT development process, reducing overall costs. It would also help to tackle complexity; a scary thought that plagues most who consider MT. Tony added an intuitive, easy to navigate user interface and extremely powerful analytics to make it easier for users to visualise what was happening to their data and what affect it was having on their MT engines during the building and customizing process.

Later, after a platform was developed that could not only match but also outperform traditional systems, KantanMT decided to address the challenge of translation quality evaluation.

how-to-measure-machine-translation-quality

For years translation companies and organisations battled with post-editors about Machine Translation output evaluation. The basic metrics that tell you how your engine is likely to perform, do not always offer a good indication about how much post-editing will be needed. Together with a team from The CNGL Centre for Global Intelligent Content, KantanMT developed an algorithm which assigns quality scores for each segment (or sentence) translated by a KantanMT engine. This means that Project Managers can accurately scope a project and determine how much of the output needs to be post-edited (if any).

KantanMT’s approach to MT has shaken up the language industry and left many companies questioning why they paid so much for MT in the past. Traditional MT providers are now quickly following in KantanMT’s footsteps and developing online portals for Machine Translation that try to mimic KantanMT.com. In the past six months alone, five vendors have released SaaS based SMT solutions. However, many still are pushing their consultancy services and saying that the same quality will never be achieved using online portals. KantanMT’s customers say different, indeed all of these have moved from traditional services to the web based approach. I guess time will tell which approach stay ahead…

 

 

 

 

 

 

 

 

 

 

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.