This 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:
- 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.
- 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.
- 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.
- 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).
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 (firstname.lastname@example.org)
Louise Irwin, KantanMT (email@example.com)