KantanMT 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 such an offbeat, unconventional and uncommon career?
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.