It’s coming to the end of a successful year and we are beginning to look back on our partnerships. We understand that being part of a network of experts, practitioners, solution providers and thought leaders is a very powerful tool for success. So, we were delighted catch up with Laura Brandon, Executive Director for the Globalization and Localization Association (GALA) to find out more about GALA, its purpose, benefits of membership and future plans. 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.
Welcome to Part II of the Q&A blog on How Machine Translation Helps Improve Translation Productivity. In case you missed the first part of our post, here’s a link to quickly have a look at what was covered.
Tony O’Dowd, Chief Architect of KantanMT.com and Louise Faherty, Technical Project Manager presented a webinar where they showed how LSPs (as well as enterprises) can improve the translation productivity of the language team, manage post-editing effort estimations and easily schedule projects with powerful MT engines. For this section, we are accompanied by Brian Coyle, Chief Commercial Officer at KantanMT, who joined the team on October, 2015 to strengthen KantanMT’s strategic vision.
We have provided a link to the slides used during the webinar below, along with a transcript of the Q&A session.
Please note that the answers below are not recorded verbatim and minor edits have been made to make the text more accessible.
Question: We are a mid-sized LSP and we would like to know what benefits would we enjoy if we choose to work with KantanMT, over building our own systems from scratch? The latter would be cheaper, wouldn’t it?
Answer (Brian): Tony and Louise have mentioned a lot of features available in KantanMT – indeed, the platform is very feature-rich and provides a great user experience. But on top of that, what’s really underneath KantanMT is the fact that it has access to a massive computing power, which is what Statistical Machine Translation requires in order to perform efficiently and quickly. KantanMT has the unique architecture to help provide instant on-demand access at scale.
As Louise Faherty mentioned, we are currently translating half a billion words per month and we have 760 servers deployed currently. So if you were trying to develop something yourself, it would be hard to reach this level of proficiency in your MT. Whilst no single LSP would probably need this total number of servers, to give you an idea of the cost involved, that kind of server deployment in a self-build environment would cost in the region of €25m.
We also offer 99.99% up time with triple data-centre disaster recovery. It would be very difficult and costly to build this kind of performance yourself. Also, with this kind of performance at your client’s disposal, you can offer Customised MT for mission critical web-based applications such as eCommerce sites.
Finally, a lot of planning, thought, development hours and research has gone into creating what we believe is the best user interface and the platform for MT, which also has the best functionality set with extreme ease of integration in the market place. So, it would be difficult for you to start on your own and build your own system that would be as robust and high quality as KantanMT.com.
Question: Could you also establish KantanNER rules to convert prices on an eCommerce websites?
Answer (Louise Faherty ): Yes, absolutely! With KantanNER, you can also establish rules, convert prices and so on. The only limitation with that being is that the exchange range will of course fluctuate. But there could be options as well of calculating that information dynamically – otherwise you would be looking at a fixed equation to convert those prices.
Question: My client does not want us to use MT because they have had bad experience in the past with Bing Translate – what would convince them to use KantanMT? How will the output be different?
Answer (Tony O’Dowd): One of things that you have to recognise in terms of using the KantanMT platform is that you are using MT to build customised machine translation engines. So you are not going to create generic engines (Bing Translate and Google Translate are generic engines). You would be building customised engines that are trained on the previous translations, glossaries that you clients have provided. You will also be using some of our stock engines that are relevant to your client’s domain.
So when you combine that, you get an engine that will mimic the translation style of your client. Indeed, instead of generic translation engines, you are using an engine that is designed to mirror the terminology and stylistic requirements of your client. If you can achieve this through Machine Translation, you will see that there is a lot less requirement for Post-Editing, and this is one of the most important things that drives away translators from using generic systems or broad-based systems and that’s why they choose customised systems. Clients and LSPs have tested the generic systems as well as customisable engines and found that cloud-based customisable MT add a value, which is not available on free, non-customisable MT platforms.
End of Q/A session
The KantanMT Professional Services Team would once again like to thank you for all your questions during the webinar and for sending in your questions by email.
Have more burning questions? Or maybe you would like to see the brilliant platform translate in a live environment? No problem! Just send an email to firstname.lastname@example.org and we will take care of the rest.
Want to stay informed about our new webinars? You can bookmark this page, or even better – sign up for our newsletter and ensure that you never miss a post!
This 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:
- 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.
- 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.
- 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.
- 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).
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 (email@example.com)
Louise Irwin, KantanMT (firstname.lastname@example.org)
The ‘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.
KantanMT 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.
To hear more about optimizing or improving the scalability of your engine please contact Louise Irwin (email@example.com).
Communication 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.
KantanMT had an exciting year as it transitioned from a publicly funded business idea into a commercial enterprise that was officially launched in June 2013. The KantanMT team are delighted to have surpassed expectations, by developing and refining cutting edge technologies that make Machine Translation easier to understand and use.
Here are some of the highlights for 2013, as KantanMT looks back on an exceptional year.
Strong Customer Focus…
The year started on a high note, with the opening of a second office in Galway, Ireland, and KantanMT kept the forward momentum going as the year progressed. The Galway office is focused on customer service, product education and Customer Relationship Management (CRM), and is home to Aidan Collins, User Engagement Manager, Kevin McCoy, Customer Relationship Manager and MT Success Coach, and Gina Lawlor, Customer Relationship co-ordinator.
KantanMT officially launched the KantanMT Statistical Machine Translation (SMT) platform as a commercial entity in June 2013. The platform was tested pre-launch by both industry and academic professionals, and was presented at the European OPTIMALE (Optimizing Professional Translator Training in a Multilingual Europe) workshop in Brussels. OPTIMALE is an academic network of 70 partners from 32 European countries, and the organization aims to promote professional translator training as the translation industry merges with the internet and translation automation.
The KantanMT Community…
The KantanMT member’s community now includes top tier Language Service Providers (LSPs), multinationals and smaller organizations. In 2013, the community has grown from 400 members in January to 3400 registered members in December, and in response to this growth, KantanMT introduced two partner programs, with the objective of improving the Machine Translation ecosystem.
The Developer Partner Program, which supports organizations interested in developing integrated technology solutions, and the Preferred Supplier of MT Program, dedicated to strengthening the use of MT technology in the global translation supply chain. KantanMT’s Preferred Suppliers of MT are:
To date, the most popular target languages on the KantanMT platform are; French, Spanish and Brazilian-Portuguese. Members have uploaded more than 67 billion training words and built approx. 7,000 customized KantanMT engines that translated more than 500 million words.
As usage of the platform increased, KantanMT focused on developing new technologies to improve the translation process, including a mobile application for iOS and Android that allows users to get access to their KantanMT engines on the go.
KantanMT’s Core Technologies from 2013…
KantanMT have been kept busy continuously developing and releasing new technologies to help clients build robust business models to integrate Machine Translation into existing workflows.
- KantanAnalytics™ – segment level Quality Estimation (QE) analysis as a percentage ‘fuzzy match’ score on KantanMT translations, provides a straightforward method for costing and scheduling translation projects.
- BuildAnalytics™ – QE feature designed to measure the suitability of the uploaded training data. The technology generates a segment level percentage score on a sample of the uploaded training data.
- KantanWatch™ – makes monitoring the performance of KantanMT engines more transparent.
- TotalRecall™ – combines TM and MT technology, TM matches with a ‘fuzzy match’ score of less than 85% are automatically put through the customized MT engine, giving the users the benefits of both technologies.
- KantanISR™ Instant Segment Retraining technology that allows members near instantaneous correction and retraining of their KantanMT engines.
- PEX Rule Editor – an advanced pattern matching technology that allows members to correct repetitive errors, making a smoother post-editing process by reducing post-editing effort, cost and times.
- Kantan API – critical for the development of software connectors and smooth integration of KantanMT into existing translation workflows. The success of the MemoQ connector, led to the development of subsequent connectors for MemSource and XTM.
KantanMT sourced and cleaned a range of bi-directional domain specific stock engines that consist of approx. six million words across legal, medical and financial domains and made them available to its members. KantanMT also developed support for Traditional and Simplified Chinese, Japanese, Thai and Croatian Languages during 2013.
Recognition as Business Innovators…
KantanMT received awards for business innovation and entrepreneurship throughout the year. Founder and Chief Architect, Tony O’Dowd was presented with the ICT Commercialization award in September.
In October, KantanMT was shortlisted for the PITCH start-up competition and participated in the ALPHA Program for start-ups at Dublin’s Web Summit, the largest tech conference in Europe. Earlier in the year KantanMT was also shortlisted for the Vodafone Start-up of the Year awards.
KantanMT were silver sponsors at the annual 2013 ASLIB Conference ‘Adopting the theme Translating and the Computer’ that took place in London, in November, and in October, Tony O’Dowd, presented at the TAUS Machine Translation Showcase at Localization World in Silicon Valley.
KantanMT have recently published a white paper introducing its cornerstone Quality Estimation technology, KantanAnalytics, and how this technology provides solutions to the biggest industry challenges facing widespread adoption of Machine Translation.
For more information on how to introduce Machine Translation into your translation workflow contact Niamh Lacy (firstname.lastname@example.org).
The 35th ASLIB conference opens today, Thursday 28th November and runs for two days in Paddington, London. The annual ‘Translating and the Computer Conference’ serves to highlight the importance of technology within the translation industry and to showcase new technologies available to localization professionals.
KantanMT was keen to have a look at how technology has shaped the translation industry throughout history so we took a look at some of the translation technology milestones over the last 50 years.
The computer has had a long history, so it’s no surprise that developments in computer technology greatly affect how we communicate. Machine Translation research dates back to the early 1940s, although its development was stalled because of negative feedback regarding the accuracy of early MT output. The ALPAC (Automatic Language Processing Advisory Committee) report published in 1966, prompted researchers to look for alternative methods to automate the translation process.
In terms of modern development, the real evolution of ‘translation and the computer’ began in the 1970s, when more universities started carrying out research and development on automated translation. At this point, the European Coal and Steel Community in Luxemburg and the Federal Armed Forces Translation Agency in Mannheim, Germany were already making use of text related glossaries and automatic dictionaries. It was also around this time that translators started to come together to form translation companies/language service providers who not only translated, but also took on project management roles to control the entire translation process.
Developing CAT tools
Translation technology research gained momentum during the early 1980s as commercial content production increased. Companies in Japan, Canada and Europe who were distributing multilingual content to their customers, now needed a more efficient translation process. At this time, translation technology companies began developing and launching Computer Assisted Translation (CAT) technology.
Dutch company, INK was one of the first to release desktop translation tools for translators. These tools originally called INK text tools, sparked more research into the area. Trados, a German translation company, started reselling INK text tools and this led to the research and development of the TED translation editor, an initial version of the translator’s workbench.
The 1990s were an exciting time for the translation industry. Translation activities that were previously kept separate from computer software development were now being carried out together in what was termed localization. The interest in localizing for new markets led to translation companies and language service providers merging both technology and translation services, becoming Localization Service Providers.
Trados launched their CAT tools in 1990, with Multiterm, for terminology management and the Translation Memory (TM) software Translators Workbench in 1994. ATRIL, Madrid launched a TM system in 1993 and STAR (Software, Translation, Artwork, Recording) also released Transit, a TM system in 1994. The ‘fuzzy match’ feature was also developed at this time and quickly became a standard feature of TM.
Increasingly, translators started taking advantage of CAT tools to translate more productively. This lead to a downward pressure on price, making translation services more competitive.
As we move forward, technology continues to influence translation. Global internet diffusion has increased the level of global communication and has changed how we communicate. We can now communicate in real-time, on any device and through any medium. Technology will continue to develop, and become faster and more adaptive to multi-language users, and demand for real-time translation will drive the further developments in the areas of automated translation solutions.
KantanMT is presenting a two-part blog series on the history of Machine Translation to give our readers a better understanding of the industry and where KantanMT fits in within the grand scheme of things. In our last post, The History of MT Pt.1 KantanMT presented the key stages in the history of MT during 1945-1979. In this post KantanMT highlights the major developments from 1980 to the present day.
The 80s and 90s…
While the EC’s Machine Translation project EUROTRA continues, Japan launches a state supported Machine Translation research programme in 1982. Japanese manufacturing witnesses a surge in the number of English-Japanese MT projects as a result. In Stuttgart in 1984, Trados is founded and becomes the first company to roll out translation memory technology (Multiterm (1992) and Translator’s workbench (1994)). In the same year IBM begins research on using “slot” grammars for Machine Translation. In 1987 the first ever Machine Translation Summit is held in Tokyo, Japan and at a conference in 1988 IBM reports on its experiments in Statistical Machine Translation (SMT) with the Canadian Hansard corpus. The feasibility of SMT becomes a major research topic and represents a break from the traditional Rule-Based methods.
The 1990s begin with Michael Blekhman establishing the first university course on Machine Translation at Kharkov State University. In 1996 Systran offers free translation of small text segments on the internet. iTranslator, the first commercial internet Machine Translation service is launched by Lernout & Hauspie in 1998 while in Dublin, ALPNET launches one of the localization industry’s first language technology integration services.
The 00s and beyond…
In 2001, the US National Institute of Standards and Technology introduces its Open Machine Translation evaluation system. The aim of OpenMT is to help improve Machine Translation technologies. Language Weaver is established in California in 2002 to produce Statistical Machine Translation systems and in 2003 the ISI team wins DARMA’s speed MT competition with, you guessed it, a Statistical Machine Translation engine. In 2004 TAUS is established and the state funded OpenTrad project is rolled out in Spain – the scheme aims to develop Machine Translation engines for the different languages in Spain.
In 2006, the European Commission launches EuroMatrix which aims to develop Machine Translation engines for European language pairs and in 2007, Moses, the Statistical Machine Translation system is launched and incorporated into EuroMatrix. 2008 sees the introduction of text/SMS translation for mobile phones by NEC and in 2009, Apptek combines Statistical Machine Translation with traditional Rule-Based models to produce a hybrid MT system.
There is also large-scale development in cloud technologies after the turn of the century and in 2012, KantanMT is launched as a cloud based Statistical Machine Translation platform.
By providing a Statistical Machine Translation Service in the cloud, KantanMT is drawing from developments throughout the rich history of the Machine Translation industry and carrying the torch into the future.
At KantanMT, we are working to change the future of the Machine Translation industry. As we create a new generation of MT technologies, it is important to acknowledge the work of earlier generations. In this blog series, we are going to take you through some of the key stages in the history of Machine Translation and talk about how KantanMT is contributing to its future.
Thanks to the folks at TAUS for providing such a detailed timeline on their website (link at the end of this post) to help us in writing this post! This first post focuses on developments from the 1940s to the end of the 1970s.
The 40s and 50s…
ENIAC (Electronic Numerical Integrator and Computer), the world’s first electronic general purpose computer is built in 1945 (see above image). In 1947, The Cold War between the West and Soviet Union begins and involves a computer technology race as both sides try to keep one step ahead of the other. In 1949, Director of the Natural Sciences Division at the Rockefeller Centre Warren Weaver introduces the idea of Statistical Machine Translation (SMT).
The 1950’s begin with the appointment of the first Machine Translation researcher Yehosha Bar-Hillel at MIT Boston. Shortly after this the first conference on Machine Translation is staged. Among the conference’s attendees is professor at Georgetown Léon Dostert (remember him from our blog The US and MT?) and he begins working with IBM on a practical experiment to see if Machine Translation is accomplishable.
The Georgetown-IBM Experiment is demonstrated publicly in 1954 and involves the IBM 701 rig translating 250 lexical items with 6 rules from Russian into English. In 1959, France introduces CETA (a centre for Machine Translation research) and the first book on the topic for general consumption, An Introduction to Machine Translation, is published by Emile Delavenay in Paris, France.
The 60s and 70s…
In 1960 the US Air force translates Russian to English with a 70,000 word dictionary using IBM technologies. The decade features the development of a number of research bodies and associations; the Association for Machine Translation and Computational Linguistics in USA (1962) and the TAM MT research group at University of Montreal (1965).
In 1966, the Automatic Language Processing Advisory Committee (ALPAC) finds that Machine Translation cannot compete with human translation and research funding for Machine Translation should be cut. 1968 brings Systran, the first official commercial Machine Translation company.
The 1970s begin with the French Textile Institute translating abstracts from and to French, Spanish, English, and German using the translation automation system TITUS. Logos Corporation begins development on a rules-based English to Vietnamese translation engine so that the US can give military technology to the South Vietnamese, however the US pulls out of Vietnam in 1973 and the Logos engine is never deployed on a full scale.
The European Commission in 1976 begins to develop a Systran English-French Machine Translation system. The end of the 1970s sees Machine Translation systems being rolled out by a number of governments and companies. For example, SIEMENs task Logos with developing a German-English system for telecoms manuals and the first Soviet Machine Translation Programme, AMPAR, is launched. EUROTRA, a high-spec Machine Translation system for the then-member languages of the European Community begins development in 1978.
In our next post, we will look at the key stages in the development of Machine Translation from the 1980s to the present day. It is in this period that SMT begins to develop and we will see how KantanMT is helping to shape the future of this branch of Machine Translation.