What’s New in eCommerce in 2016? More Localization and Better Machine Translation

ECommerce_Whitepaper_KantanMT Download

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

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Summary:

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.

Download the KananMT white paper on eCommerce today!

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Improving workflow integration and efficiency with KantanAPI

What is the KantanAPI?

KantanAPI enables KantanMT clients to interact with KantanMT as an on-demand web service. It also provides a number of different services including translation, file upload and retrieval and job launches.

With the KantanAPI  you not only have the opportunity to integrate KantanMT into your workflow systems but also the ability to receive on-demand translations from your KantanMT engines. All these services make the experience with Machine Translation as seamless as possible.

Accessing KantanAPI

Please Note: The API is only available to KantanMT members in the Enterprise Plan.

To access the KantanMT API you will first need your ‘API token’. This token can be found in the ‘API’ tab on the ‘My Client Profiles’ page of your KantanMT account.

Once you have your token you can use the API in a number of ways

  1. Using the API tab on the ‘My Client Profiles’ page in the KantanMT Web interface
  2. Using the REST interface via HTTP GET or POST requests
  3. Using one of our various connectors, which are built using our KantanAPI

For more details on implementing your API solution via the REST interface, please see the full API technical documentation at the following link:

How to use KantanAPI?

Login into your KantanMT account using your email and your password.

You will be directed to the ‘My Client Profiles’ page. You will be in the ‘Client Profiles’ section of the ‘My Client Profiles’ page. The last profile you were working on will be ‘Active’.

If you wish to use the ‘KantanAPI’ with another profile other than the ‘Active’ profile. Click on the profile you wish to use the ‘KantanAPI’ with, then click on the ‘API’ tab.

API tab

You will be directed to the ‘API Settings’ page. Now click on the ‘Launch API’ button.

Launching API

A ‘Launch API’ pop-up will now appear on your screen asking you ‘Are you sure you want to launch the API?’ Click ‘OK’.

launch Pop-up alert

The ‘API Status’ will now change from ‘offline’ to ‘initialising’, the ‘Launch API’ button will now change to ‘Launching API’ .

Launching API

When your KantanAPI launches the ‘API Status’ will now change from ‘initialising’ to ‘running’, the ‘Launching API’ button changes to ‘Shutdown API’ and you should now be able to click on the ‘Translate’ button.

API running

Type the text you wish to translate in the text box and click on the ‘Translate’ button.

Translating

The translated text will now appear in the ‘Translated Text’ box. If you wish to make any changes to the translated text simply place the cursor inside the ‘Translated Text’ box and make the changes. Save these changes by clicking the ‘Retrain Engine’ button.

Retrain Engine

Test if your engine was successfully retrained by clicking the ‘Translate’ button. The retrained text will now appear in the ‘Translated Text’ box.

If you don’t wish to retrain your engine and you are happy with the translated text in the ‘Translated Text’ box. You may continue translating other text or shut down your KantanAPI by clicking the ‘Shutdown API’ button.

When you click the ‘Shutdown API’ button a pop-up will now appear asking you ‘Are you sure you want to shout down the API?’ Click ‘OK’.

Shutdown Pop-up alert

The ‘Shutdown API’ button will now change to ‘Terminating API’, the ‘API status’ will now change from ‘running’ to ‘terminating’ and you shouldn’t be able to click on the ‘Translate’ or ‘Retrain Engine’ button.

Terminating API

You will now be directed back to the initial screen on the API Settings page.

API settings page

 

Additional Support

KantanAPI™ is one of the various machine translation services offered by KantanMT to improve  productivity for our clients and also enable them to be more efficient. For more information on KantanAPI or any KantanMT products please contact us at info@kantanmt.com.

For more details on the KantanMT API please see the following links and the video below:

3 Steps to Integrating SDL Trados Studio with KantanMT

SDLSDL Trados Studio is one of the most popular Computer Aided Translation (CAT) tools available on the market today, and is used by thousands of Language Service Providers (LSPs) and Translators worldwide.

To accommodate the high numbers of SDL Trados Studio users, the KantanMT development team released a new and improved KantanAPI Connector™, which is compatible with the latest versions of SDL Trados Studio (2011, 2014). The beauty of using this connector means you can quickly and easily configure both your SDL Trados Studio account and your KantanMT account, so there is a straightforward and seamless integration between both platforms.

As a member of the KantanMT Community, using SDL Trados Studio 2011 or 2014 you can launch and shutdown your KantanMT engines and retrieve translations on demand via the API from your KantanMT account, all you need to provide is your KantanMT account name, token and profile.

Once you have your KantanAPI Connector™ token, it’s a simple three step process to set up the integration.

Integrating SDL Trados Studio with KantanMT

Step 1: Login to the SDL Open Exchange App Store and Download the Installer

To download the app you will need a valid SDL Trados Studio license. Login to the SDL Translationzone App store using the same email address and password you use for SDL Trados Studio.

Step 2: Launch SDL Trados Studio

As soon as you have downloaded and installed the SDL Trados Studio installer from SDL’s Translationzone, you will need to launch Trados Studio.

Step 3: Select and Run the KantanAPI Connector

The KantanAPI Connector™ will appear in the list of plugins available for download, making it very straightforward to input your API token and select the profile that you want to use. The connector is completely free to download and requires the .NET Framework 3.5 to run correctly.

By using SDL Trados Studio you can easily access the KantanMT features available within the SDL Trados Studio interface based on your KantanMT subscription plan.

KantanAPI Connector Token

What do I do if I don’t have a KantanAPI Connector™ token?

Simply contact the KantanMT Sales Team (sales@kantanmt.com) to get your unique KantanAPI Connector token.

About KantanAPI Connector™ v2.0

The KantanAPI Connector™ allows you, and other members of the KantanMT Community to interact with the cloud based MT platform; KantanMT.com. You can submit individual segments or groups of segments for translation, and receive those translations immediately. The API operates as a REST web service, this means that a client program needs only to be able to perform HTTP GET requests to interact with the API. So, the API is not limited to interacting with clients developed using a particular programming language or operating system.

Read the press release: KantanMT Announces Faster SDL Trados Studio 2011 and 2014 Integration

Product Sheet: KantanAPI Connector™

Download the Trados 2015 Plugin

The KantanMT Team would love to hear about your experience using the KantanMT/SDL Trados Studio connector. Please send your feedback or questions to Louise (louisei@kantanmt.com).

Language Industry Interview: KantanMT speaks with Maxim Khalilov, bmmt Technical Lead

Language Industry Interview: KantanMT speaks with Maxim Khalilov, bmmt Technical LeadThis 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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).

KantanMT Industry Webinar 5 Challenges of Scaling Localization for the 21st Century_Webinar

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 (peggy.lindner@bmmt.eu)

Louise Irwin, KantanMT (louisei@kantanmt.com)

Scalability or Quality – Can we have both?

KantanMT Engine optimization, machine translationThe ‘quality debate’ is old news and the conversation, which is now heavily influenced by ‘big data’ and ‘cloud computing’ has moved on. Instead it is focusing on the ability to scale translation jobs quickly and efficiently to meet real-time demands.

Translation buyers expect a system or workflow that provides high quality, fit-for-purpose translations. And it’s because of this that Language Service Providers (LSPs) have worked tirelessly, perfecting their systems and orchestrating the use of Translation Memories (TM) within well managed workflows that combine the professionalization of the translator industry – quality is now a given in the buyers eyes.

What is the translation buyers’ biggest challenge?

The Translation buyers’ biggest challenge now is scale – scaling their processes, their workflows and supply chains. Of course, the caveat is that they want scale without jeopardizing quality! They need systems that are responsive, are transparent and scale gracefully in step with their corporate growth and language expansion strategy.

Scale with quality! One without the other is as useless as a wind-farm without wind!

What makes machine translation better than other processes? Looking past the obvious automation of the localization workflow, the one thing that MT can do above all other translation methods is its ability to combine automation and scalability.

KantanAutoScale, KantanMT product, machine translationKantanMT recognizes this and has developed a number of key technologies to accelerate the speed of on-demand MT engines without compromising quality.

  • KantanAutoScale™ is an additional divide and conquer feature that lets KantanMT users distribute their translation jobs across multiple servers running in the cloud.
  • Engine Optimization technology means KantanMT engines now operate 5-10 times faster, reducing the amount of memory and CPU power needed so MT jobs can be processed faster and are more efficiently when using features like KantanAutoScale.
  • API optimization, KantanMT engineers went back to basics, reviewing and refining the system, which enabled users to achieve improvements from 50-100% performance in translation speed.  This meant translation jobs that took five hours can now be completed in less than one hour.

Scalability is the key to advancement in machine translation, and considering the speed at which people are creating and digesting content we need to be able to provide true MT scalability to all language pairs for all content.

KantanMT’s Tony O’Dowd and bmmt’s Maxim Khalilov will discuss the scalability challenge and more, in a free webinar for translation buyers; 5 Challenges of Scaling Localization Workflows in the 21st Century on Thursday November 20th at 4pm GMT, 5pm CET, 8am PST.

KantanMT and bmmt webinar presenters Tony O'Dowd and Maxim Khalilov

To hear more about optimizing or improving the scalability of your engine please contact Louise Irwin (louisei@kantanmt.com).

5 Reasons to Read the TAUS Review

Earlier this month, TAUS, a well-known industry think tank and resource centre for the language services industry launched its quarterly publication; the TAUS review. The new magazine with a mission is dedicated to;

“Making translation technology more prominent and mainstream throughout the globe to break language barriers and improve worldwide communication.”

KantanMT TAUS Review

KantanMT identified five key reasons that make the review an invaluable asset to any translation and localization professional. It’s thanks to these reasons that KantanMT will distribute the TAUS Review right here on the KantanMTblog.

1. Global Translation Industry news 

TAUS has mobilized writers from across the globe; Africa, Americas, Asia and Europe to discuss different trends and technologies in the language services industry. These articles can become a great reference tool for those interested in how language technologies are advancing. In this issue; Andrew Joscelyne reports from Europe; Brian McConnell gives updates from the Americas; Asian trends are covered by Mike Tian-Jian Jiang and Amlaku Eshetie reports from the southern hemisphere; Africa.

2. Research and Reports 

Recent Research in MT is pretty exciting stuff, those that consider themselves language industry veterans like Luigi Muzii remember a time when machine translation predictions were overestimated. But what was once an unrealistic assumption is now changing as “neural networks and big data” are bringing a new frontier to natural language processing. Luigi Muzii gives an overview of the ‘research perspective’, highlighting current trends in research and linking to some interesting ACL winning papers, which introduce MT decoders that do not need linguistic resources.

3. Unique Insights

TAUS Review offers unique insights into the translation industry by incorporating use cases and perspectives from four different personas; the researcher, the journalist, the translator and the language expert, each one with their own different views and opinions on the importance of global communication and breaking down language barriers. In this issue, Jost Zetzsche, Nicholas Ostler, Lane Greene, and Luigi Muzii share their perspectives.

KantanMT especially enjoyed  Jost Zetzsche’s view of making “machine translation translator-centric” where the translator is at the centre of the MT workflow. One of the examples he lists for making this possible, “dynamic improvements in MT systems” is available to KantanMT clients.

4. Language Technology Community 

The opinions and thoughts that come from each contributor are neatly wrapped in one accessible place, and when coupled with the directory of distributors, events and webinars make a very useful resource for any small business or language technology enthusiast. Keep an eye out for some very interesting post-editing and MT quality webinars planned for November.

5. It’s Free! 

Holding true to the concept of sharing information and making translation technology more prominent and mainstream throughout the globe, the review is available quarterly and completely free for its readers, making it accessible to anyone, anywhere regardless of their budget.

Scroll to the end of the page to find the TAUS review on the KantanMTBlog.

Leveraging MT to Improve Productivity

KantanMT Leveraging MT in BusinessCommunication is the one of the most important elements of business, and Machine Translation is a flexible tool that can be used to facilitate communication in a wide variety of scenarios and situations. Multinationals and other companies operating globally can take advantage of Machine Translation to achieve productivity gains.

This two part blog series examines two very different examples of implementing Machine Translation. This first post will look at what multinational organizations should consider before introducing Machine Translation to their business, and the second post will discuss the productivity gains and competitive advantages that can be achieved by Language Service Providers (LSPs) who adopt MT.

What is a multinational and why should it use Machine Translation?

Multinational corporations or global businesses are organizations operating in more than one country or region. The concept of an ‘international company’ has been around for hundreds of years, going back to the trading companies, which were established in the 1700s. Outside political agendas, their main purpose was to trade in spices and other commodities throughout Asia and Europe exposing traders to different languages and cultures.

Hundreds of years later, global communication is common place as more businesses operate internationally. There are no boundaries, and companies with worldwide operations require a constant flow of multilingual communication in order to maintain relationships between global employees, customers and stakeholders.

Multinational organizations typically have two types of content; external and internal. External content is created and released to the public; corporate documents, investor information, Corporate Social Responsibility (CSR) and marketing communications. On the other hand, internal content is created for use within the company, this is usually in the form of email and chat communications, memos and other internal documents.

To Translate or not to translate

Organizations without an in house translation team, often outsource the translation of external content to a reputable LSP. This ensures a guaranteed level of quality for the translation, and it also means that the process of localization is more efficient and cost effective. This is because, over time language assets in the form of translation memories, can be built up and leveraged to off-set the cost of future translations.

Internal content, however, is mostly comprised of communications between departments; emails, chats and information on sales and marketing activities. These are usually not translated professionally for a number of reasons:

  • Cost – the volume to be translated can make costs unmanageable
  • Confidentiality – managing sensitive information is more difficult
  • Real-time translation – emails and chat conversations generally requires real-time speed

As an example, if a company is headquartered in the United States, but operates in both Asia and Europe there is a very high possibility that more than one language is used in the company’s internal communication.

Multinational companies often select working languages that must be used for internal communications and department managers are sometimes required to have a certain level of proficiency in the company’s designated working languages, which usually includes English.

Large organizations like the United Nations also have official languages. In this case, documents are not published until a translation has been prepared in each official language.

So, what happens when an email with a client’s product specifications and sales information is sent to a group of employees who speak different languages? Some of those readers may have limited knowledge of the language being used, and only be able to understand the communication, but are not familiar enough with the language to write a coherent response. This can result in them responding in their native language. Suddenly, a single conversation thread contains more than one language, with a greater potential for miscommunication.

Why use Machine Translation?

Multinationals with global operations often have issues with the quantity and flow of internal information between departments operating in different languages. If the corporate headquarters uses a different language than its global subsidiaries, corporate documents need to be translated into each language as the internal information moves down the organizational hierarchy.

Machine Translation is a solution that can provide an instant, understandable ‘gist’ of internal information across a company operating in different languages and the use of MT can serve two purposes:

  • Documents that require a professional human translation are easily identified
  • Internal documents can be translated instantly so employees can get an understanding of the content

In order to understand internal content, employees often might use an open source MT solution such as Google Translate. While this is useful, it does not take into consideration any proprietary jargon or writing styles specific to the organization, and it also raises the question of confidentiality.

Challenges of MT

Many organizations may be interested in taking steps to deploy their own MT systems rather than outsourcing translation jobs or asking bilinguals in the company to do ad hoc translations. Those considering MT have two options; develop their own in house system or use a cloud-based subscription model.

Implementing any new process has challenges and MT is no exception. Some challenges traditionally associated with implementing MT systems are:

  • High costs
  • Complex technology
  • Long deployment times

How should an MT system be integrated?

Before going ahead with an MT solution, an organization needs to carefully consider what it hopes to achieve from implementing Machine Translation. The company should evaluate all the perceived benefits thoroughly, including managing any and all expectations about using Machine Translation.

Organizations thinking of implementing MT should ask:

  • What is its purpose? – Will MT be used as a management tool to improve internal communication and productivity, or to make decisions on what documents require professional outside translation? The purpose should be clearly defined at the outset.
  • Do we have enough language assets to build high quality engines? Bilingual language assets are a key ingredient for building MT engines. The quality of the training data will have a direct impact on the MT engines output “garbage in, garbage out”.
  • Should we invest in building our own system or buy a cloud-based subscription service? MT systems can be rule-based (RBMT), statistical (SMT) and hybrid. In house development of a propriety MT system requires a heavy technology, HR and training investment, unless those assets are readily available. Cloud-based subscription models do not require such a heavy initial investment and are often more cost effective than developing and managing an in house MT system.
  • Is the Machine Translation option scalable? How many language combinations will be needed? If each language pair requires its own unique engine, how simple is it to build additional engines with new language combinations? Scalability will be determined by translating capacity and the ability to add new language combinations, this would be especially important when entering different language markets or expanding the business to new regions. The MT solution should align itself with the company’s long term goals.
  • How will MT be integrated into everyday workflows?  Users need to be able to easily access translation functions through their existing applications like email or the company intranet system to make it accessible and viable.
  • What indirect costs and planning will be involved? RBMT and hybrid systems require qualified linguists or language experts to develop and manage the engines. SMT systems use algorithms to identify probable translations based on the frequency, therefore, storage capacity is essential for the large volumes of training data required. Cloud options eliminate the need for in house technology investment, but extra costs might be incurred for going over the subscription plans, similar to the minutes allowance with mobile phone usage.

In carefully answering these questions, any organization planning to implement MT can stay focused on using the most cost-effective solution and achieve productivity gains with less miscommunication and more time savings.

The next part of this blog will look at how LSPs can leverage Machine Translation technology for productivity gains and competitive advantage.