Free Webinar: How Machine Translation Improves Translation Productivity

KantanMT Quick to deploy Machine TranslationIf you are in the language service industry, you are undoubtedly on the lookout for ways in which you can improve the productivity of your team – more translated words in less time – that’s what drives your clients as well as you. Automated Machine Translation (MT) seems to be the logical step forward in today’s world of content explosion and tightening deadlines. However, for most Language Service Providers (LSPs), the challenge lies in the actual implementation of this sophisticated technology.

For this reason, it is important that no matter what translation management tools you use, it should be integrated with a powerful MT engine that is reliable, scalable, flexible, and can be trained and re-trained constantly for maximum efficiency and quick turnaround times.

In today’s fast-paced world of content explosion on the Internet, the need for translating this organically growing content with the help of machines has become inevitable. While post-editing the machine translated content will always be required, choosing the right MT features will ensure that translators do not spend countless frustrating hours on those edits.

In this Kantanwebinar, The KantanMT Professional Services Team, Tony O’Dowd and Louise Faherty (Quinn) will show how you can improve the translation productivity of your team, and manage effort estimations and project deadlines better with a powerful MT engine.

During this webinar you will learn:

  • Translation challenges (co-ordinating and managing translation projects)
  • About the necessity of Machine Translation to be competitive
  • How KantanMT.com can be integrated with other Translation Management Systems

Register for KantanWebinar

To find out how KantanMT.com can improve your company’s translation productivity, send an email to demo@kantanmt.com

Video: Machine Translation Success – Milengo and KantanMT

Global Business with KantanMTMachine translation applications have sky rocketed, and we as consumers demand content to be readily available in our native language. We make purchases online quickly, and expect those purchases delivered to our doors regardless of language and shipping destination.

Common Sense Advisory identified that three quarters of online consumers prefer to buy in their own languages. This is significant for online business, and as such companies are aware that a localized product or service available online means a much greater customer pool, which in turn leads to more sales and a bigger return for stakeholders.

The global ecommerce market is growing at approx 30% per year and is estimated to reach $2 trillion in sales in 2015 (Democratization of Ecommerce Report, BigCommerce).

There is one big ‘wall’ still standing between more sales revenues and happy customers, and that is ‘multilingual support’. Traditional multilingual support requires a heavy investment in translation and localization workflows, not to mention a plethora of specialists needed to provide linguistic support.

However, ‘Big data’, computing capabilities and the cloud are creating unique possibilities to avoid such heavy investments and companies that choose to embrace these new opportunities are reaping the rewards.

KantanMT’s Founder and Chief Architect, Tony O’Dowd and Deepan Patel, Machine Translation Solutions Architect at Milengo Ltd. discuss the opportunities offered by implementing a cloud based machine translation solution. They examine Milengo’s experience using KantanMT to optimise its translation supply chain, and illustrate, with examples; how the leading translation company uses KantanMT.com to achieve excellent results in ongoing MT projects for some of the world’s major companies

Key Takeaways:

  1. Manage User Expectations: Clear communication with the client about the process, workflow and expected results will ensure trust and confidence in the project. Even without a pilot test, Milengo still managed to localize a web shop with 780,000 Danish words to Swedish in 17 days.
  2. Think to Scale: The localization process must always be scalable, each example for; software documentation (Interactive Intelligence), ecommerce (Netthandelen) and automotive parts data required an automated solution that could be scaled.
  3. Customise It: MT customisation can fulfil a wide variety of localization needs. Not only is it more cost efficient (Netthandelen achieved 62% cost savings), it enables engine retraining quickly, and improves its ability generate higher quality translations.

To learn how you can generate meaningful business intelligence that lets you manage and improve the ROI from Machine translation, contact us for a free consultation and/or personalised platform demonstration.

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)

New Breakthroughs In MT Technology – Webinar Q+A

Last Thursday KantanMT hosted a webinar which introduced some of the latest breakthroughs in machine translation technology. Joined by Maxim Khalilov, Sr. Machine Translation Lead at bmmt, Tony O’Dowd (Founder, KantanMT.com) explained how these technologies are helping Language Service Providers and Enterprises to develop and manage MT systems in a completely transparent environment.

BuildAnalytics offers MT developers deep insight into their MT engines using distributed scoring, Gap Analysis, Rejects Report and Timeline features. Incorporating these insight tools into the MT development process means a shift away from the “Black Box” that many MT users have experienced, and a move to MT developer empowerment.

After the presentation, both presenters engaged in a Question and Answer session. The Q+A including the questions which were not addressed during the session are listed below:

 

Q+A

1Q. KantanMT can only be used in your remote server? Is it possible to have an own server running it (because we have a confidentiality agreement with our customers)?

A. Yes – You can install a Run-time license of KantanMT.com onto your own server.

 

2Q. Is KantanMT an “out-of-the box” solution or do you have to program it to fit your particular environment?

A. KantanMT.com is a platform that you can use to customise, improve and deploy MT systems for your company. While KantanMT.com comes with over 5 billion words of stock training data sets, our clients typically customise their own engines for their content and translation style. This ensures that the engine is finely tuned to their content, reducing post-editing and improving quality and consistency.

 

3Q. Is there support from KantanMT in building your MT engine or is it mainly a (self-explanatory) DIY process?

A. KantanMT.com provides all the tools and utilities you need to customise your own engines – however, some clients want us to build these engines for them. This is a service engagement and can be organised by contacting sales@kantanmt.com. There are also a range of quick-start videos available for free that walk you through the steps in building your own engine.

 

4Q. Do you provide consultant services concerning guidelines/training for post editing?

A. Yes, KantanMT provides both consultancy and training for companies that wish to deploy MT within their organisations. You can contact sales@kantanmt.com for more pricing and timing information in regards to these services.

 

5Q. If I train my KantanMT engine, do you have access to this material? Are you using it?

A. No – everything you upload to the KantanMT.com servers is full encrypted and stored under your account name and secure password. No one else has access to your files or training material. KantanMT will never re-task, re-purpose or re-publish your data in any form and should you cancel your account you data will be removed from our servers automatically.

 

6Q. What differentiates you from your major competitors?

A. KantanMT.com is a complete MT development and management platform, and enables our clients to customise, improve and deploy high quality production ready MT systems. The platform is 100% cloud-based, easy to access and extremely fast to operate! No other platform provides the detailed data analytics and visualisation we do – KantanAnalytics is designed to provide quality estimation scores for every segment, similar to Translation Memory fuzzy match scores it is revolutionising the MT market. Kantan BuildAnalytics helps our clients customise, improve and deploy high quality engines in a fraction of the time traditional MT providers deliver. Coupled together with GENTRY, PEX and Kantan Pre-processor technology, KantanMT is simply the most technologically advanced MT system available in the market today.

 

7Q. Does KantanMT (Machine Translation) work well with Trados Studio Professional 2014?

A. Yes – You can translate TRADOS Studio files directly or can use our Studio plug-in (developed in conjunction with SDL) to provide in-place real-time MT services.

 

8Q. Are all language combinations equally suitable for MT?

A. No – all languages are not created equally when it comes to MT. Romance languages tend to do better than Germanic languages and Japanese has a range of language specific nuances. That is why KantanMT provides specially handling for a range of languages to make them easier for our clients MT systems to translate. For example, we use our own re-ordering models for German and Japanese to ensure higher consistency and transformation into these morphologically challenging languages.

 

9Q. As I understand it, my data is added to data provided by KantanMT. How do you ensure that terminology in your data does not create inconsistent translations?

A. We add more weight in the data model to your training data at all times. This ensures that it is always picked up first during translation and styling. Additionally, you can upload your terminology/glossary files directly into the training data so that guarantees the selection of your terminology.

 

10Q. What is the most words that you have processed for a single customer in a day?

A. The most words processed by a client in a single session is just under 2m words and this took approximately 3 hours. The throughput of a single instance of an engine is around 400K words per hour so it is possible to process 9.6m words per 24 hours. However, this can be improved substantially by adding more servers and a load balancer. For each additional server, you can process just under 10m words a day and this improves linearly as you add more servers.

 

11Q. What kind of methods are you using to get a quality estimation score? Is it comparable to what the QuEst project generates?

A. The KantanAnalytics QE scores were developed in conjunction with the CNGL (Centre of Next Generation localisation) and the Kantan Development team at DCU, Dublin. It uses a series of factors taken directly from the MOSES engine (nbest list, word alignment, complexity scores etc.) to determine the estimated quality for each segment. In this regards it is similar in approach to QuEst.

 

12Q. What is the feedback from KantanMT system users who handle translation in the Double bite languages, such as Chinese, Japanese, Korean?

A. Our first client is one of the largest companies in the world who selected us for the English to Simplified Chinese quality. We support all DBCS languages at present.

 

13Q. I would like to know if your MT systems are only cloud-based?

A. Yes – the KantanMT.com platform is 100% cloud-based. You need so special hardware, no special software, just an account name and secure password to start using our platform.

 

14Q. Do you mean 30% cost savings (cost reduction) or 30% increase in productivity? If cost savings, what was the increase in productivity?

A. 30% is cost savings – post-edit rate improved from 350 words per hour to 550 words per hour.

You can watch the presentation section of the webinar here:

 

For more information about KantanMT and MT technologies, please visit http://www.kantanMT.com

Happy MT’ing

 

 

Q&A: Chrome Data

Chrome data, KantanMT
Chrome Data Solutions, a joint venture between Chrome Systems, Inc. and Autodata Solutions Content Group became an early adopter of KantanMT technology after signing up to the platform in November 2012. The company, which has over 25 years experience in automotive data and software, offers high-value vehicle content and applications for a global market.

Manager of Translation Services at Chrome Data, Mohamed Hammoud speaks to KantanMT about his experience with Machine Translation and its impact at Chrome Data Solutions.

What encouraged you to search for a Machine Translation solution?

The need to deliver quality translations at competitive pricing and constantly increase our ROI drove our organisation to look at smarter ways that we could approach our language needs. We had developed proprietary tools to manage our data as well as invested in translation memory but this was not enough. A solidly-developed and content-rich Machine Translation was an important next step to help achieve our goals: improve efficiency and reuse translation memories; lower translation costs; and deliver quality localised products to our diverse automotive client base.

What were the greatest obstacles that you faced when integrating Machine Translation into the translation workflows at Chrome Data?

There were many solutions in the industry and some of the greatest obstacles included ease of integration with our own tools and cost. KantanMT responded by providing a solution with little to no start-up cost or maintenance, as well as a seamless product that was easy to use and which integrated not only with our technology but also our workflow.

How did you overcome these obstacles?

We first needed to become very familiar with the concept of Machine Translation and its application within our organisation. The great staff at KantanMT helped by providing friendly and constant support and because this product was (and is!) still being improved and developed, we benefited from seeing our wish list become an actual roadmap within the product’s development.

How has Machine Translation impacted on the translation department in Chrome Data?

Although we are very happy with Machine Translation, we are still discovering all of the benefits. Learning to integrate it with our other tools and processes to achieve optimal results will take time, but we are definitely off to a very positive start.

What role do you think Machine Translation will play in the translation industry over the next ten years?

You can never replace human interaction within the translation process but you can assist it and continually improve upon it. The smarter the Machine Translation, the faster and more improved results in the final product and with real time need for products to become localised in an ever-increasingly global community, there is no turning back.

chrome data logo

For more information about how Machine Translation can help you grow your business, or to sign up for a free trial, please visit KantanMT.com or send a quick email to our MT Success Coach, Kevin McCoy, who will happily answer any of your questions.

KantanMT host regular webinars on Tuesdays and Wednesday for those getting started with Machine Translation, and for those who wish to improve their Machine Translation Engine quality:

Register here:

Getting Started with Machine Translation>>

Improve your MT Engine quality>>

RTS Case Study

RoundTable Studio Logo

About RoundTable Studio:
RoundTable Studio is a leading provider of translation and localisation services for the Spanish and Brazilian Portuguese language markets. With production centres in Brazil and Argentina, and a small business and project management unit in Spain, RTS and its team of more than 50 linguists, project managers and technical experts serve a worldwide client base across many vertical markets including IT, business & finance, and manufacturing. The company’s quality ethos and high level of customer service are significant factors in their success and reputation within the industry, and their openness to new technology is proving to be an important factor in their increased competiveness and company growth.

Situation:
Global collaboration and increased competition are formidable factors which are affecting both the translation industry and the industries it serves. Consequently, LSP’s like RTS are beginning to search for scalable, cost effective solutions which will help them bridge the gap between meeting expectations of ever faster turnaround times and maintaining high quality standards at affordable rates.

Laura Grossi, Localisation Engineer at RTS confesses that “sometimes there are just not enough linguists to carry out jobs” – this is a feeling that many LSPs in search for a capacity solution are echoing.

Solution:
RTS has been active in Machine Translation since 2005 when the company started working with one of its key clients on a focused Machine Translation initiative.  This relatively early exposure led to the company building substantial technical and practical expertise, as well as a firm belief that Machine Translation has an important role to play in the future of the industry.  In addition to collaborating with client specific Machine Translation programs, RTS also realized the need to find a solution to help integrate Machine Translation selectively into its tools portfolio.  However, finding an Machine Translation provider which would give them the freedom to manage and control their Machine Translation activity in a strategic manner proved to be a difficult task. RTS assessed and tested a number of Machine Translation solutions before being introduced to KantanMT in October 2012. The company was immediately impressed by the simplicity, flexibility, control and evaluation metrics that the KantanMT platform provided.

Using KantanMT quality evaluation metrics, including BLEU, TER and F-Measure, RTS was able to expand its knowledge and improve engine quality and subsequently output quality. Grossi notes that a favourite feature of the platform is the KantanWatch™ reporting function, a measurement tool allowing her to track engine quality over time, helping the team to become more adept at choosing training data to reduce set up times and increase productivity.

Measuring KantanMT using KantanWatch

Strategic and selective deployment of Machine Translation within its workflow is enabling RTS to improve capacity and flexibility of translation throughput.  In combination with a focused investment in developing resources and capacity for post-editing in order to maintain the same quality standards as with human translation, the company considers it a critical tool for improving efficiency and growing business, as well as offering its customers optimal overall value and service.
Grossi concludes that RTS “has increased its productivity on certain translation jobs significantly”, and by implementing KantanMT, “has increased capacity levels to take on translation jobs they otherwise would have had to turn down”.
Adopting KantanMT technology has helped RTS successfully create a foundation for ensuring its future business competitiveness.

Read how Matrix Communications AG introduced Machine Translation into their workflow KantanMT.com >>

DCU Students practice MT

Dorothy-Kenny
Dr Dorothy Kenny

Lead by Dr. Dorothy Kenny and Dr. Stephen Doherty, over 50 DCU post graduates are learning to train Machine Translation (MT) engines in a variety of different languages and getting first-hand experience of using the latest KantanMT technology. Using DGT translation memories from the Acquis Communautaire (an open source translation memory directory provided by the European Commission), students are learning not only how to build engines and translate documents, but also how to evaluate their engines’ quality by utilising KantanMT’s automatic quality evaluation metrics.

“There is rapid technological shift towards more automated translation within the industry”, said Dr. Kenny, “and we want our students to graduate fully aware of the latest fusion of Machine Translation technology and the cloud. It’s important that they are comfortable with the use of Machine Translation as a way to improve translation productivity. ”

Dr. Kenny and Dr. Doherty offered encouraging feedback from their experiences while teaching with KantanMT. They particularly liked  ‘the intuitive and easy to navigate user interface and the speed of use’ which meant that the majority of students were able to build their first engine within the space of a one hour lab.

Dr. Kenny and Dr. Doherty stressed the importance of proper education when it comes to translation technology. They aim to encourage their students to use Machine Translation technology in a proactive rather reactive manner in order to “empower them as professionals”.