Following our launch of KantanNeural™ engines as part of our KantanFleet™ repository of pre-built MT engines, we received a number of questions and interest around the product. To address these questions, we asked Tony O’Dowd, CEO and Chief Architect of KantanMT.com a few questions about the Neural Machine Translation engines on KantanMT, the features and benefits of these engines and the impetus behind launching KantanNeural. Continue reading
Our ‘5 Questions’ series is coming to an end, and this week we are happy to introduce you to Pat Nagle, who talks about some cool technology, his favourite KantanMT feature and lots more. The series aims to give you a better insight into the thoughts and ideas of the people at KantanMT. Continue reading
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
Have you ever wondered who are people behind KantanMT?
We are thrilled to announce a series of posts where we will give 5 questions to each of our team members. These questions will delve a little deeper into their thoughts about technology, language and personal interests!
We are delighted to introduce Laura Casanellas, who bravely accepted the challenge of going first.
Machine 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.
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
- 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.
- 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.
- 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.
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 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.
Thank 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.