As a team of people with an unbridled passion for innovations in the Machine Translation industry, Monday’s news about Reverie Technologies, a Bengaluru-based startup bagging a $4M investment did not come as much surprise to us. This brilliant news serves to highlight once again that in the ever-changing world of retail marketing and globalization, any business with plans to accelerate their products into global markets needs to localize their content for enhanced user experience. This goes on to drive global revenues and increase brand equity in existing and new markets. Continue reading
KantanISR technology enables KantanMT members to perform instant segment retraining using a pop-up editor. The technology is designed to permit the near-instantaneous submission of post-edited translations into a KantanMT engine so that KantanMT members can submit segments for retraining, hence bypassing the need to completely rebuild the engine.
KantanISR was developed with usability, efficiency and productivity in mind as members simply need to login to their KantanMT account, go to their main dashboard and submit new training segments using the KantanISR Editor. This adding of high quality training data to a KantanMT engine will improve the translation quality of that engine and reduce post-editing requirements.
- 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 ‘KantanISR’ with another profile other than the ‘Active’ profile. Click on the profile you wish to use the ‘KantanISR’ with, then click on the ‘Training Data’ tab.
- You will be directed to the ‘Training Data’ page. Now click on the ‘IRS’ tab.
- The ‘KantanISR’ wizard will now pop-up on your screen.
- Add the source language text in the ‘Source’ text editor fields. Add the corresponding target language text in the ‘Target’ text editor fields.
- Then click on the ‘Save’ button if your happy with your retraining data. If not click the‘Cancel’ button.
- When you click the save button a ‘KantanISR successful’ pop-up will appear on your screen, click the ‘OK’ button and you will be directed back to the ‘Training Data’ page.
Using KantanISR through KantanAPI
Please Note: The KantanAPI is only available to KantanMT members in the Enterprise Plan.
Members’ can also get the benefit of KantanISR through KantanAPI by using HTTP
GET requests. The API expects:
- A user authorisation token (‘API token’) which can be gotten by clicking on the ‘API’
- The name of the client profile you wish to use.
- A source segment and its target segment in the languages specified when profile was created.
To learn more about KantanISR or get help with KantanMT technologies, please contact us at email@example.com. Hear from the Development team on why KantanISR increases productivity and efficiency for KantanMT customers.
What is KantanAnalytics?
KantanAnalytics generates quality estimation scores for automated translations generated by KantanMT engines. The better the KantanAnalytics scores – the better the quality performance of a KantanMT engine as it means translations are more accurate and fluent and require less post-editing effort.
KantanAnalytics creates a detailed project management report of all segments within a KantanMT project. This includes segment-by-segment quality estimation scores in addition to other useful project statistics such as word, character, placeholder and tag counts.
KantanAnalytics can help Project Managers make the right decision as it predicts the cost and post-editing effort for Machine Translation projects. Prioritizing the right translations through segment quality estimation will yield the fastest possible project turn-around.
How to use KantanAnalytics
- 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 view the ‘KantanAnalytics’ of another profile other than the ‘Active’ profile. Click on the profile you wish to view the ‘KantanAnalytics’ of, then click on the ‘Client Files’ tab.
- You will now be directed to the ‘Client Files’ page.
- Click on the ‘Analyse’ tab.
- A ‘Launch Job’ pop will now appear on your screen saying your job has been launched. Note the ‘Job ID’ Click on the ‘OK’ button on the ‘Launch Job’ pop-up.
- You will receive an email notification stating that your job has been launched in the email address you use to register on KantanMT.com.
- You will also receive an email notification when the job has been completed.
- Click on the ‘KantanMT’ tab and select ‘My Jobs’ from the drop down menu.
- You will now be directed to the ‘My Jobs’ page.
- Search for the job using the ‘Job ID’, you can use the ‘Search Bar’ or go to the ‘#’ column and scroll till you find the ‘Job ID’.
- Click on the analyse Icon beside the job to view the job analysis
- You will now be directed to the ‘KantanAnalytics Report’ page for the job.
- To exit out of the ‘KantanAnalytics Report’ page click on the ‘Back to My Jobs’ button.
- You will be directed back to the ‘My jobs’ page.
To download the ‘KantanAnalytics’ of the job click on the download icon
For more details on KantanAnalytics please see the following video below:
To learn more about KantanAnalytics or get help with KantanMT technologies, please contact us at firstname.lastname@example.org or visit the KantanMT website. Hear from the Development team on the benefits of KantanAnalytics
In this team we deal with setting up and testing new engines for our clients and offering an exceptional customer experience. If the description below sounds like you, send your CV and cover letter to email@example.com
- Localization Engineer/MT Project Coordinator
Scope of job
- The Localization Engineer/MT Project Coordinator provides client support in the setting up and linguistic testing of a MT engine for KantanMT clients.
- Project deliverables fully tested and certified SMT systems built using the KantanMT platform. This will include data preparation for training, testing, Language Quality Review and deployment of final approved engine into production.
- Project tasks include evaluation, preparation, compilation, testing, delivery, troubleshooting and back up of the project files.
- Resources Managed: Ongoing MT Analysis, Translation Memory and Machine Translation platforms.
- Reports directly to the Professional Services team lead.
- Evaluate and prepare project deliverables (software, help, docs, websites and others) for translation following set quality standards. This process typically includes the use of CAT tools as well as writing instructions for translators
- Format, test, deliver and back up the project deliverables following set quality standards
- Completion of projects on time and in line with quality standards
- Provide daily feedback to the Team Lead on project tasks status
- Manage Translation Memories and Machine Translation systems
- Customising, Improving and deploying project Machine Translation engines
- Methodical analysis of client feedback to provide meaningful improvements to output
- Support towards colleagues in other departments or linguists on CAT tools
Software applications used
- Windows platform: MS Office, MS Visual Studio, Alchemy Catalyst, InstallShield, RoboHelp, RoboHTML, HTMLQA, web browsers, Notepad++, Wordfast & related programs (e.g. S-Tagger), SDL Trados Studio, Idiom WorldServer, other CAT tools, KantanMT.com
- Macintosh platform: ResEdit, Resourcer, Installer VISE, Stuffit InstallerMaker, Appleglot, Powerglot, MPW.
- Others: VMware, Perl, Python, C++ or C#
- Computer Degree or other in similar discipline
- Computer experience in the translation/localization industry
- A second language is preferable
- Understanding of localization engineering processes and tools
- Experience with recent Windows operating systems. Macintosh or Linux operating systems knowledge is a plus
- Scripting experience is a plus
- Enthusiastic, organized, flexible and attention to detail
- Ability to manage stress and collaborate
- Good communication skills
Please send your CV and a cover letter to firstname.lastname@example.org and tell us why you’re the right candidate for this job.
KantanMT Founder and Chief Architect, Tony O’Dowd was recently featured in one of Ireland’s major national newspapers; The Irish Times.
The author of the news article, Olive Keogh is a business journalist who specialises in writing about innovative Irish enterprises and startups. With Olive’s kind permission, we are republishing the Irish times article.
“It’s not widely known at home but Ireland has developed an international reputation for research in statistical machine translation. Trinity, DCU and UL are all recognised worldwide and 120 PhD students have graduated here with skills in the field in the last five years. That’s more than in any other country in Europe,” says Tony O’Dowd the man behind KantanMT, a new scalable, high-speed machine translation system based on the Moses decoder and the Amazon Web Services and Cloud Computing infrastructure.
O’Dowd has spent almost 30 years in the software localization sector with companies such as Lotus Development Corporation and Symantec. Xcelerator, the company behind KantanMT, is O’Dowd’s second start-up, but he was also involved in the formation of FIT, a training organisation set up in 1998 to provide IT skills and training for the long-term unemployed.
Economics of the Cloud
“We are leveraging the Moses MT decoder and multiple streams of research from the Centre for Global Intelligent Content to make statistical machine translation (SMT) technology available to the masses,” he says.
“Traditional SMT systems are slow, expensive to deploy, time-consuming to customise and complex to manage. In short, not for the faint-hearted. I wanted to harness the economics of the cloud to solve these problems. Using hundreds of high-powered cloud-based severs to convert training data into data models also accelerated the process of customisation and the development of SMT engines.”
O’Dowd points out that in addition to the cost factor, traditional SMT solutions can produce translations of dubious quality. By focusing on advanced natural language processes and data processing algorithms, KantanMT also addresses these quality issues.
“Because of the costs involved, SMT tends to be used by large organisations with big budgets and plenty of people available to work on the system. The KantanMT platform removes this expense and complexity and makes it a far more practical and usable tool for businesses both big and small. Our clients can customise, improve and deploy their own engines in a matter of days,” O’Dowd says.
O’Dowd took his first steps as an entrepreneur in 2000 when he set up Alchemy Software Development. It quickly became a leading player in the software localization sector with over 27,000 licences in use worldwide. This success didn’t go unnoticed. The company was sold to the largest privately owned localization service provider, Translations.com, in March 2007.
Prior to setting up Alchemy O’Dowd was technology manager for Symantec Corporation Ireland and responsible for establishing the organisation’s Asian localization hub in Japan. He was also executive vice-president of Corel Corporation and spent three years as a lecturer in Trinity College Dublin teaching microprocessor design and assembly language programming.
O’Dowd began working on the idea for KantanMT in 2011 while on a year “off” to retrain himself on cloud-based technologies. He employed an MBA student to do detailed research into the barriers preventing companies using SMT and says the major leap forward in computing and storage capacity provided by the cloud enabled him to build a platform for SMT systems that would have been inconceivable without it.
Xcelerator recently raised €1.1 million in seed funding from venture capital company Delta Partners and the Enterprise Ireland High Potential Start Up fund. Early versions of KantanMT were given away free to kill competition and grab market share but first revenues (based on a usage pricing model) began flowing this time last year and O’Dowd says it is now profitable. A second round of funding is planned for later this year.
The company currently employs 11 people in its offices in Dublin and Galway, but this is expected to rise to 20-25 by the end of 2015. Its focus is the export market and its biggest customers are independent software vendors from industries such as ecommerce, finance and electronics. The company also provides MT services to the language industry.
School of Hard Knocks
“Starting your first business is definitely daunting as everything is new and you’re travelling down every road for the first time,” O’Dowd says.
“Next time around there is a lot of commonality and because you’ve learned by engaging with the school of hard knocks, you’re better at anticipating the problems and meeting the challenges. You also have a better network of contacts, you’re less frazzled when things don’t go right and you can actually grow the business faster and at a higher level. You also get a better hearing from the funding community as they view you as a safe pair of hands.”
KantanMT is based in the Invent Building at DCU and O’Dowd says the resources and expertise provided by the Invent team were instrumental in getting KantanMT.com off the ground.
“KantanMT.com is the fastest growing SMT platform in the localization industry today. So far over 80.5 billion words have been uploaded to the platform as training data and more than 750 million words have been translated by our clients. When you consider this has all happened in the last nine months, the company is rapidly becoming one of the biggest translation hubs in the market,” O’Dowd says.
The original article was published on Mon, Apr 27, 2015
Email email@example.com to learn more about how the KantanMT platform operates, or if you would like to set up a personalised demo with Tony.
KantanMT caught up with Milengo’s Machine Translation Solutions Architect, Deepan Patel earlier this week for a quick chat about his experience using machine translation. Next Month, Deepan will be joining Tony O’Dowd in a free live webinar, to talk about how Milengo maximized it’s ROI for machine translation.
KantanMT: Can you tell me a little about yourself and, how you got involved in the industry?
Deepan Patel: To be honest, I sort of fell into the localization industry but I am certainly very glad that I did! I am a Modern Languages graduate from the University of Oxford which provided a very traditional approach to translation, certainly a million miles away from the realities of life in the localization industry.
I moved to Berlin after graduating in late 2008 and within a year I was fortunate enough to be accepted on a trainee program by my current employer Milengo Ltd, a language services provider which was founded in 2005. The first ever project I ever worked on was one that involved the customization of statistical machine translation (SMT) engines for a customer wishing to test the long-term viability of incorporating machine translation and post-editing into their localization operations.
It was a tremendous experience for both myself and Milengo; it was really that initial project that has laid the foundations for the MT-related services that we now offer. The main focus of my work at Milengo relates to testing and deploying customized machine translation and post-editing workflows for clients requiring a completely outsourced MT solution.
KMT: How has MT affected or changed your business models at Milengo?
DP: I believe that having machine translation and post-editing as part of our service spectrum has lent us a significant competitive advantage. This was very apparent in September last year when we were approached by an eCommerce company with quite a formidable challenge: namely, they had 19 days in which to launch a new web shop for Sweden and around 780,000 words that needed to be localized from Danish into Swedish. And of course they had a very tight budget!
Through the experiences that we have gained running large-scale machine translation and post-editing projects over the years, we were able to confidently provide a compelling MT-based workflow solution which fell within our client’s budget and would deliver high-quality translated content before their launch date. When providing their reasons for choosing us as for that project, it was our confidence in stating that we could deliver in time that was the main factor. Without our experience with machine translation, we would not have been able to win that project – it is as simple as that. We were able to deliver high-quality localized content within budget and before the initial deadline request. And now we enjoy regular work from this client, localizing all the updates to their product descriptions across three language pairs.
So in essence, MT has enabled us to win those large-scale projects where customer budgets are limited, turnaround time is crucial but quality expectations are high, that we may not have stood a favourable chance of winning previously.
KMT: How do you use machine translation for your clients?
DP: When answering this question I must take pains to emphasize that our MT service offerings always involve post-editing. For one of our clients within the IT domain, we localize the online help to their software products across five language pairs using customized engines that have been built using their own language assets. The requirement there is to deliver high-quality localized content at a significant cost reduction to a human-only translation model. For this particular customer we have achieved cost savings of between 27 – 40 % depending on the language pair.
For another of our clients within the automotive sector, we have built custom MT systems across 3 language pairs to provide a cost-effective but high-quality localization solution for their huge volume of parts data. The initial challenge presented to us was to localize around 300,000 words of this data within a fairly tight timeframe – though not as challenging as our eCommerce client! We were first able to demonstrate the viability of customized machine translation and post-editing for this type of content via our free Machine Translation and Post-editing (MT-PE) feasibility study, after which point we deployed our workflow solution for their three requested target languages. Again for this customer, we have implemented cost savings of between 25 – 40% when compared to the traditional translation model and are enjoying continued business from them.
The third main scenario where we apply MT-PE is for our eCommerce client that I mentioned in my response to your previous question. They add new products to their web shop on a weekly basis and their very repetitive product descriptions need to be localized as soon as possible, so the content can go “live” on the different language sites. Together with this customer we are now focusing on automating as much of the project process as possible with regard to transfer of content via API connectors and using our customized MT systems as a fully-integrated part of their localization project workflow.
For all of these clients, we have been able to offer tiered-pricing packages based on the premise that the more content that we post-edit and feed back into their MT systems during re-training cycles, the better the system will perform on future projects. Consequently we can offer lower rates for localization at defined intervals. Really it is all about being able to demonstrate the long-term cost-savings possible with a customized MT-PE solution.
KMT: What advice can you give to translation buyers, interested in implementing a machine translation workflow strategy?
DP: Well, firstly I would encourage translation buyers to evaluate whether they have the time, budget and most importantly the relevant personnel within their organization to develop a custom MT solution, or whether it would make sense to turn to external help in the form of MT tech providers like KantanMT, or LSPs such as Milengo who would additionally be able to provide post-editing solutions as well.
I would also encourage translation buyers to evaluate how MT can be applied in different usage scenarios. For example, it would certainly be worth investigating MT-PE for large volume, highly repetitive content (user manuals, support documentation, catalogue data) where you can achieve significant cost-savings and quicker turnaround without compromise on the language quality (with excellent post-editors of course). Another worthwhile scenario for MT would be if your company produces a lot of short life-cycle or customer support content which needs to be available in the languages of your customers as quickly possible, and where transfer of meaning takes precedence over linguistic quality.
Thirdly I would ask the respective translation buyer to examine the state and volume of any language assets that they can use for customizing MT systems. Do you have enough of a training corpus to build MT systems which produce good quality MT output? Have your language assets been maintained well enough to ensure as much consistency in translation as possible? Remember that an MT system will only ever be as good as the material you use to train it. Again here external help may be useful in terms of applying data cleaning and normalization to the training corpus before you get round to building your MT systems.
Finally, I would always advise prospective translation buyers to consider the wider impact benefits of incorporating MT into their localization practices. The more you make use of your custom MT systems and more post-edited content you incorporate into system re-training cycles, the better your systems will perform. This of course leads to greater productivity benefits and reduced costs for localization. Which in turn means that you should free up more of your budget to turn your attentions towards localizing content that was previously considered too cost-prohibitive.
Thank you Deepan, for taking time out of your busy schedule to take part in this interview, and we look forward to hearing more from you in KantanMT’s upcoming partner webinar. The webinar, Maximizing ROI for Machine Translation will be held on Wed, Mar 11, 2015 3:00 PM – 4:00 PM GMT.
Selçuk Özcan, Transistent’s Co-founder has given KantanMT permission to publish his blog post on Translation Quality. This post was originally published in Dragosfer and the Transistent Blog.
Literally, the word quality has several meanings, one of them being “a high level of value or excellence” according to Merriam-Webster’s dictionary. How should one deal with this idea of “excellence” when the issue at hand is translation quality? What is required, it looks like, is a more pragmatic and objective answer to the abovementioned question.
This brings us to the question “how could an approach be objective?” Certainly, the issue should be assessed through empirical findings. But how? We are basically in need of an assessment procedure with standardized metrics. Here, we encounter another issue; standardization of translation quality. From now on, we need to associate these concepts with the context itself in order to make them clear.
As it’s widely known, three sets of factors have an effect on the quality of the translation process in general. Basically, analyzing source text’s monolingual issues, target text’s monolingual issues and bilingual issues defines the quality of the work done. Nevertheless, the procedure should be based on the requirements of the domain, audience and linguistic structure of both languages (source and target); and in each step, this key question should be considered: ‘Does the TT serve to the intended purpose?’
We still have not dealt with the standardization and quality of acceptable TT’s. The concept of “acceptable translation” has always been discussed throughout the history of translation studies. No one is able to precisely explain the requirements. However, a further study on dynamic QA models needs to go into details.There are various QA approaches and models. For most of them, acceptable translation falls into somewhere between bad and good quality, depending on the domain and target audience. The quality level is measured through the translation error rates developed to assess MT outputs (BLEU, F-Measure and TER) and there are four commonly accepted quality levels; bad, acceptable, good and excellent.
The formula is so simple: the TT containing more errors is considered to be worse quality. However, the errors should be correlated with the context and many other factors, such as importance for the client, expectations of the audience and so on. These factors define the errors’ severity as minor, major, and critical. A robust QA model should be based upon accurate error categorization so that reliable results may be obtained.
We tried to briefly describe the concept of QA modeling. Now, let’s see what’s going on in practice. There are three publicly available QA models which inspired many software developers on their QA tool development processes. One of them is LISA (Localization Industry Standards Association) QA Model. The LISA Model is very well known in the localization and translation industry and many company-specific QA models have been derived from it.
The second one is J2450 standard that was generated by SAE (Society for Automotive Engineers) and the last one is EN15038 standard, approved by CEN (Comité Européen de Normalisation) in 2006. All of the above mentioned models are the static QA models. One should create his/her own frameworks in compliance with the demands of the projects. Nowadays, many of the institutes have been working on dynamic QA models (EU Commission and TAUS). These models enable creating different metrics for several translation/localization projects.
About Selçuk Özcan
Selçuk Özcan has more than 5 years’ experience in the language industry and is a co-founder of Transistent Language Automation Services. He holds degrees in Mechanical Engineering and Translation Studies and has a keen interest in linguistics, NLP, language automation procedures, agile management and technology integration. Selçuk is mainly responsible for building high quality production models including Quality Estimation and deploying the ‘train the trainers’ model. He also teaches Computer-aided Translation and Total Quality Management at the Istanbul Yeni Yuzyil University, Translation & Interpreting Department.
Read More about KantanMT’s Partnership with Transistent in the official News Release, or if you are interested in joining the KantanMT Partner Program, contact Louise (firstname.lastname@example.org) for more details on how to get involved.