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.
The innovative Machine Translation features released by KantanMT, along with our contribution towards improving automated translation workflow has earned us the reputation for being thought leaders in the industry. A few months back, we released a white paper on what global companies can expect to see in 2016 for Machine Translation (MT). Continue reading
KantanMT recently published a white paper on what global companies can expect to see in 2016 for Machine Translation (MT). The MT industry is rapidly changing and moulding itself to the technical needs and globalization requirements of the present day. Our white paper puts forward six major MT trends that all businesses need to heed in order to stay relevant and ahead of their competitors.
If the post-Black Friday sales numbers are anything to go by, there’s no question any more that the face of eCommerce is changing, and with it, the brick-and-mortar retailers have started rethinking their business strategy. As this news piece about Scotland experiencing a major dip in shoppers goes on to prove, demand for online shopping will increase substantially in 2016. This in turn means that the need for content localization and translation for eTailers (online retailers) will be even more pressing during the coming new year. As the often quoted Common Sense Advisory report points out, 72.4% of consumers are more likely to buy from a site, which is in their native language. Indeed, localization is no longer a good-to-have feature – it is now a must-have for all eCommerce businesses that aim to sell their products globally.
Chris Bishop, Managing Director of Microsoft Research, Cambridge, UK points out that “by 2026 we will have ubiquitous, human-quality translation among all European languages, thereby eliminating the language barrier throughout Europe.” Bishop’s prediction does not sound far off the mark at all when we take into account the fact that in the past ten years, Machine Translation (MT) has improved by leaps and bounds. Early MT was rules-based (RBMT) and required sets of linguistic rules, and it worked moderately well within a prescribed domain. However, this was resource intensive and cost prohibitive for many.
By 2026 we will have ubiquitous, human-quality translation among all European languages, thereby eliminating the language barrier throughout Europe
Chris Bishop, Managing Director of Microsoft Research, Cambridge, UK
The turning point for using MT in business came with the advent of the Internet, the SaaS model and the open source development model for software. These new changes in technology helped build the foundation for Statistical Machine Translation (SMT) research, and subsequently the open source development of the Moses Decoder. Moses enabled researchers and private companies to commercialise Statistical MT and develop it to the custom solutions it is today. The year of 2016 and beyond, will see further research in the fields of Natural Language Processing (NPL), Deep learning and machine learning, contributing directly to immense improvements in the fields of Custom MT.
The KantanMT Business Team published a new white paper, which provides an in depth understanding of how eTailers in 2016 will be affected by Machine Translation, and also goes on to discuss how Custom Machine Translation when compared to generic MT systems, will emerge as the clear winner in solving eTailing localization issues in the coming year.
Here are some of the highlights how MT will evolve in 2016 for eTailers:
Machine Translation is no longer a luxury. It is an essential component as a Tier 1 application to support global business. The purpose of this paper is to highlight how Machine Translation and more importantly Custom Machine Translation technology has come of age, in terms of quality, speed and scalability. During 2016 and beyond eTailers need to ensure that they review their globalization strategies to reflect these advances in technology, so they can maximise their global growth potential.
It’s a fact, infiltrating new markets is the key to increasing profits, and the first item on any company’s internationalization checklist should be to make sure it communicates product information in a way its target customers can understand.
Leading on from the 2006 research, CSA’s updated survey in 2014 was based on a sample of three thousand global respondents, and it reinforced earlier results by showing that 55% only buy from websites in their native language. This jumped dramatically to 80% in cases where the buyers English language ability is limited.
When it comes to selling internationally, tapping into new revenue streams demands translated content. But, what happens when you have thousands of product descriptions that need to be localized into a plethora of languages?
This is where the fun begins for localization teams with well-established traditional translation workflows in place. Their existing method seems fine…but when it’s time to scale up, this is when cracks in the process begin to appear.
The translation workflow works best when it matches the scale and velocity for the content created whether it is product descriptions, manuals or online help documentation.
We have heard a great deal of arguments for and against machine translation and one of the most well known against arguments is “the quality is rubbish, sentences translated by machine translation are garbled and incomprehensible”. We in the language technology field hear this frequently and often shudder in disbelief at how these conclusions have been reached.
Generic or free machine translation systems in most cases do not produce great results, expecting such a system to produce publishable quality MT results or using it as benchmark for all MT systems is akin to extracting blood from a stone. Achieving good MT output takes time, care and the ability to customise the MT system properly.
Any company that is serious about breaking into international markets should also be serious about their MT strategy. They should be considering a customised MT solution that is tailored to their needs, not just by going for a cheap and/or supposedly free option.
Statistical machine translation is based on machine learning and pattern recognition. Segments with multiple word phrases or n-grams as they are known are identified with probability algorithms that select the most probable translation match. Generic or free MT systems typically have been built on a broad mix of content styles and types. This means it’s much harder for the MT system to identify the most likely or even relevant matches in generically built engines.
When the MT system is customised specifically for content that comes from a single domain, such as product descriptions for a specific categories e.g. Home and garden, fashion or electronic devices, the syntax, style and phraseology used will make sure that when an MT match is generated there will be a higher probability that the match will be closer to the desired output, resulting in a much more accurate translation.
Of Course Machine Translation can save costs – if done properly, significant savings can be made. But, saving costs is often not the end goal for implementing a serious MT strategy. The real gains come from increasing productivity without a compromise in quality. Why translate 2000 words a day when you can machine translate and post-edit 8000 words with no loss of quality? Really it can be done! See an example first hand (Netthandelen’s case study PDF download).
When it comes to eCommerce and selling hundreds of products online the words to be translated are counted in billions not thousands, and without MT, traditional localization budgets would become more and more expensive, so MT is really the only practical solution. But, if MT is considered a way to save money by cutting corners then it is doomed to fail from the outset.
It will fail because it’s not sustainable, the effort and costs required to fix bad quality MT output are too great, and if fixing is neglected by publishing the content as is, it will result in angry customers who shop elsewhere – and they will, as the choice available now is greater than ever before!
Email email@example.com if you have questions or want to learn more about how Machine Translation works for product descriptions.
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
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.