5 Questions with Louise Faherty

Louise Faherty KantanMT Professional Services

For our fourth post in the ‘5 Questions’ series, we are very excited to introduce you to Louise Faherty, Technical Project Manager of the Professional Services team at KantanMT. This series of interviews aim to give you a deeper insight into the people at KantanMT. Continue reading

5 Global Companies Localizing Right

Globalization Localization KantanMT

Globalization is no longer a modern phenomenon. With accelerating technological advancements in every sphere including communication, manufacturing and transport, even Globalization 2.0 is a somewhat dated concept. So what’s next? Continue reading

5 Questions with Brian Coyle

Brian Coyle KantanMT

In our third post of the ‘5 Questions’ series, we are delighted to introduce you to Brian Coyle, Chief Commercial Officer at KantanMT. The ‘5 Questions’ is a series of interviews that aims to give you a deeper insight into the people at KantanMT.

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5 Best Practices for Checking Machine Translation Quality

Excel KantanMT

You have your finger on the pulse of latest technologies, and you are proud to use the latest automated technology for your localization needs. But, sometimes it might feel like you are still stuck in the 90s when it comes to reviewing your Machine Translation (MT) output for quality – especially, if you are using spreadsheets to collate your reviewers’ feedback on segments.

Traditionally language quality review for MT involves the Project Managers (PMs) sending copies of a static spreadsheet to a team of translators. This spreadsheet contains lines of source and target segments, with additional columns where the reviewers score the translated segments according to a set of predefined parameters.

Once the spreadsheets are sent off to the reviewers, PMs are completely in the dark – with no idea how the reviewers are progressing, when they might complete the review, or if they have even started the project.

If that sounds tiring, imagine what the PM has to go through!

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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

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

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.

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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)