The globalised make-up of the car industry, means automated translation is an important tool for those working in the automotive industry. KantanMT has helped clients use Machine Translation to efficiently translate technical documentation, motor part catalogues and how-to manuals, whilst automotive websites, such as ChromeData use KantanMT to translate content, so it can give detailed vehicle info and specifications for thousands of websites and dealerships around the globe.
The automotive industry has always been one of change. That change is leading to fundamental shifts in car technology and how users interact with them. In 2016, a typical car coming off the production line will contain 100 million lines of code. 20 million of those lines of code are required just to run a standard navigation and infotainment system. This increasing complexity inevitably leads to increasing level of customisation.
Changing Automotive Industry
While technology continues to advance, car manufacturers are increasingly looking at it as an area of differentiation. As manufacturers explore ways of delivering superior performance, implementing software that can be updated regularly, similar to that of a mobile phone, will enter mainstream usage in our cars. Technology centric car companies such as Tesla are already utilising such conveniences and it is inevitable more will follow.
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:
eTailers will use a combination of only CMT or CMT and Human Post-Editing to reach new markets ahead of their competitors
With increased multilingual customer demand for products, content translation will find support in auto scaling
Custom Machine Translation will be used more widely as eCommerce customers expand globally
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.
The rapidly evolving, dynamic marketplace today has created an enormous spike in the demand for Machine Translation (MT) in a number of industries. According to a new study by Grand View Research, the global Machine Translation market is expected to reach USD 983.3 million by 2022. This is a huge leap from 2014, when the MT market was valued at USD 331.7 million, and this growth projection mirrors a trend in the market. Thanks to globalization, there is an increased demand for cost efficiency in translation, but the amount of linguistic knowledge and time required for translating all the content for a particular business exceeds the capacity of human translation alone.
Key Insights into the Machine Translation Market
Some of the key insights about Machine Translation that the study discusses are summed up here:
Statistical Machine Translation (SMT) is a clear winner over Rules Based Machine Translation (RBMT), when it comes to the present market requirement.
Globalization and the need to address diverse cultural groups has led to the popularity of translation technology in Asia Pacific, thus opening up new potential markets for MT providers.
Proliferation of smartphones and increasing internet penetration is expected to drive MT market growth.
Machine translation as a service (MTSaaS) makes use of SMT and is accessible via the web. This allows users to customise their MT engines with their own Translation Memories (TMs).
What this means is that, deploying an integrated MT solution will become a critical success factor for gaining market share in the future.
The study reveals major challenges for the MT industry, which includes a lack of quality translations and Quality Estimation (QE) and competition from free translation service providers. Needless to say, a well-rounded back-end knowledge base, along with efficient NLP (Natural Language Processing) capabilities and a scalable model are critical to gaining competitive advantage in the market. The MT providers need to go above and beyond their role as simply providing machine translation services; they need to become solution providers.
How is KantanMT contributing to the MT market?
The KantanMT platform offers massive competitive advantage, not only because we were one of the first entrants in the MT market, but also because thanks to our strategic market insights, we have already identified most of these challenges and developed solutions to address them. As solution providers, we use an intuitive approach that can be summed up in a few words: speed, scalability, simplicity, and security.
In a market where new products and innumerable variants of those products are being developed almost every day, it is important to have on-demand translated content ready to be deployed. KantanMT helps its clients have the first leap advantage over their competitors by translating content on the fly.
KantanMT engines have the capacity to translate 114 million words in a single day and as of 7 September, 2015, we have exceeded 2 billion translated words, with 1 billion words being translated in the last two months itself.
As a business trying to make its mark in the global MT market, it is extremely important to have a solution that has limitless scalable potential. KantanMT engines with scaling technologies such as the KantanAutoScale are devised to ensure that no matter how sudden the spike in content is, the quality and volumes of translated content will never suffer.
The power of KantanMT’s engine was summed up Tony O’Dowd, Founder and Chief Architect of KantanMT.com,
“We are only starting to see the potential growth of the Machine Translation market, and I doubt any other player can operate at this scale as flawlessly.”
Simplicity is at the very core of KantanMT. The company name itself is derived from the Japanese word for simplicity 簡単 (かんたん). KantanMT strives to take the complexity out of the user interface, while powerful MT engines do all the hard work in the back end. Easy to understand analytics can be generated through the KantanMT engines to gather insights into improving engine quality and maintaining translation quality.
Cloud based MT solutions have become the industry norm. However, security concerns are high – especially, if you are in the eCommerce industry or deal with legal information. KantanMT’s multilayered security approach protects and monitors translations ensuring all industry secrets are safe. Unlike a number of open source translating tools, you own the source as well as translated words.
One of the key findings of the Grand View Research review points out that “strategic joint ventures, coupled with mergers and acquisitions, (which) have been among the key strategies adopted” by major players in the Machine Translation industry. KantanMT recognises the importance of both industry and academic relationships in building a complete MT ecosystem.
In today’s world, the path to profits comes from global expansion, and everyone in business wants profits. With the goal of increased profits in mind, it is logical that business professionals constantly keep an eye out for new ways to expand their customer base and increase their bottom line.
In many cases, the most effective way to reach new customers is to speak their language and what better way to do this than through the translation and localization of product content into the languages, which are spoken, understood and used by the target audience.
When content is static and only needs a one-off translation, then traditional translation workflows do the job just fine, but when the content is a continuous stream of product descriptions or online help/chat content, a real-time scalable translation solution is the only feasible solution.
Machine Translation (MT) is the real-time scalable solution and the key to opening up new markets, reaching new customers and increasing profits. It is a productivity tool in the content production workflow with the potential to boost a company’s economic performance. However, a word of caution…before reaping the economic benefits of including MT in content production there are some criteria that should be carefully considered before jumping in to use Machine Translation.
Join Tony O’Dowd Founder and Chief Architect of KantanMT and Alan Houser, Co-Founder and President of Group Wellesley, Inc. as they discuss the economic arguments in favour of including Machine Translation in content production workflows.
Webinar Date: Thursday July 16th 5PM IST (Dublin), 9AM U.S. west coast and 12PM US East Coast will last approximately one hour including a Q&A session.
During this webinar, you will learn:
Potential uses of Machine Translation
How MT can drive performance to increase economic value
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.
The first part of this two-part series examined the factors a multinational organization should consider before introducing MT into their business workflows. It also described how Machine Translation can help to break down barriers to communication within these organizations, improve internal communications and increase productivity as a result.
This second post looks at the challenges of implementing MT from a Language Service Provider’s (LSP) perspective, and examines how MT could be used to improve the LSPs competitive positioning and productivity, as part of the translation business model.
The decision for an LSP to adopt Machine Translation technology is almost always strategic. LSPs have been leveraging the use of translation memories since the widespread adoption of the technology in the 80’s. Translation memories or bilingual corpora are the primary assets for training customized Statistical Machine Translation (SMT) engines.
According to Joshua Duhl, localization is important for three main reasons:
Effectively engage customers
Maintain global brand recognition
Why are LSPs using Machine Translation?
The key drivers for the introduction of Machine Translation are technology advancements and changes within the global communication dynamic – factors which are increasing the demand for real-time translations of multilingual content. In response to this demand, MT is becoming a necessity for many LSPs who want to offer their clients the full spectrum of translation and localization services.
Language Service Providers benefit from the ability to leverage their language assets (TMs) when introducing MT into their business. This greatly reduces the time it takes to get started with MT. Translation Memory has a firmly established positioning as the industry’s main translation tool, and now, because the two can be integrated, LSPs and translators can reap the benefits of combining both TM and MT technologies in a bid to increase efficiencies, and enhance their service offerings.
Before integrating MT, an LSP should take certain factors into consideration.
Rustin Gibbs, Senior Solutions Architect and Joe DiDamo, Senior Business Development Manager at Jonckers Translation and Engineering carried out a critical study on the challenges of integrating MT into the LSP workflow. I listed some challenges from the article below:
Challenges of implementing MT into the translation pipeline
Perception of MT – Machine Translation is often considered a sensitive subject. Negative perceptions based on earlier versions of the technology still remain strong. Convincing those involved with language services, on both the client and translator side that MT can be a useful translation tool will take time.
QE and Post-editing effort – Quality Estimation and calculating post-editing effort is the biggest factor hindering MT adoption. While theories and technologies for measuring quality and post-editing effort are available, there is still no widely accepted industry standard.
Training Data – the quality of freely available training data can be unreliable and sourcing or creating high quality training data is costly and time-consuming.
Language assets – even though LSPs have access to their client’s language assets, the client’s intellectual property rights mean LSPs may not be in a position to leverage these across different client projects.
Language Pair – both the language pair and domain play a part in the quality of the MT output. Translating content to and from more complicated language combinations can be less successful, as LSPs face the challenge of collecting higher volumes of quality to make sure the engine performs at an optimum level.
How should LSPs integrate an MT system?
As with multinational organizations, the decision to integrate Machine Translation should be done with a well-defined implementation strategy in place and realistic expectations about the integration.
LSPs thinking of implementing MT should ask themselves:
What are the potential costs? How will the costs and resources be managed while building and training the MT engines and which MT system best suits my needs. Do we have the resources and capacity to license the software and build our own systems or should we outsource to MT vendors?
Can the technology be implemented with as little disruption as possible within our existing localization workflow? One of the biggest challenges of implementing a new technology is an increased risk of setbacks and issues while users adjust to using the new technology. The time required for training and fixing these issues should be factored into the implementation process.
Project Managers should be able to fully understand MT technology, so they can make informed decisions when deciding on the most efficient MT solution for each individual project workflow (decisions based on domain, language pair, MT engine quality, project lead-time). PMs should also be familiar with the engine’s training data and its similarity to the writing style of the source text.
How can we leverage language assets across clients and what possibilities are available for sharing assets? Being able to share language assets will cut the costs and time required to create and collect training data. Building partnerships with organizations and companies can reduce the burden of creating language assets.
This is not an exhaustive list of points for LSPs to consider before implementing MT, LSPs should also ask some of the questions outlined in the first part of this post ‘Leveraging MT to Improve Productivity’.
Once LSPs have sat with all these questions, they should have a clear picture about their MT needs and their capacity to implement it within their business. LSPs may want to gradually introduce MT across their key domains and language pairs, and over time, increase this as demand develops.
LSPs who integrate MT will quickly see the benefits from translation economies of scale – TMs can be leveraged across translation processes, which means that they can give more value to the company.
Thinking about introducing Machine Translation in your LSP? Email Niamh (email@example.com) to set up a demo and discuss a KantanMT solution that fits your business needs!
Welcome. This is a four part blog series which will examine Porter’s core strategies for competitive advantage. During the series we will look at how these strategies can be applied to companies working in the translation industry.
Michael Porter, Harvard Business School, explains that competitive advantage occurs when an organisation “acquires or develops an attribute or combination of attributes that allows it to outperform its competitors.”
Expanding on this concept, in his book “Competitive Strategy” (1980, a book which was voted the ninth most influential management book of the 20th century) – and again in “Competitive Advantage” (1985, a book I read during my years in college) – he surmised four core strategies companies should embrace in order to create a clear and superior competitive advantage in their markets.
I thought it would be interesting to see how Machine Translation – as a growing service differentiator in the LSP world – would fit into Porter’s four strategies, and to examine if it ticks all of the Competitive Advantages check boxes!
Cost Leadership Strategy
Porter defines “Cost Leadership” as offering products or services at the lowest possible cost in the industry. The emphasis here is on cost rather than price; cost is what you purchase your products/services at and well, price is what you sell these on at – hopefully obtaining a nice profit in the process, helping your company grow and thrive. I guess in a nutshell, it’s all about avoiding operating at a loss by optimising this cost/price ratio.
But the devil of achieving that cost/price optimisation is in the detail of efficiently running a day-to-day innovative business. And by running a business that develops an attribute, or attributes, that differentiates it from its competitors. Successful companies that embrace Porter’s Cost concept must by necessity strategically vary their Cost attributes through the product/service they offer. A good example is Walmart, where they offer key items at deep discounts, while selling other products at less aggressive discounts. It is different sides of the same cost/price coin and taken holistically can be a very successful strategy. Walmart has successfully beaten off all of its major competitors in the US domestic market for decades by pursuing this particular Cost Leadership strategy.
So what’s the take-out here for Localization Service Providers (LSPs) on cost/price? Well, for the majority of translation quotes, the per-word translation-costs represent the lion’s share of the total project costs: in many cases this is as much as 85%. So while some LSPs may focus on containing the costs of their support services (such as engineer, project management, review and edit etc.), the really successful ones realise that it is by focusing on the translation-costs – that 85% of cost area – that they can gain most competitive advantage.
This reality has been manifesting itself as a significant and wholehearted move by many LSPs. Many are now moving towards Translation Automation as a cost saver. Clearly, for an LSP to embrace a “Cost Leadership Strategy”, it must be relentless in pursuing a translation automation strategy. Only by developing such a strategy will an LSP give itself the strong differentiating cost attribute that allows it to outperform its competitors.
Machine Translation is a key component of any translation automation strategy, and its use can positively impact on the translation-cost component of any localization project. For instance, one of our KantanMT members reported a 37% reduction in translation costs as a result of integrating MT into their automated translation workflow.
…Read more about Porter’s strategies in Friday’s blog. Tony O’Dowd, Founder and Chief Architect