Enterprise Ireland is again showcasing the best Irish technology and innovation at the 21st annual South by Southwest (SXSW) festival between March 11-20, 2016 in Austin, Texas – and we are excited!
So, why are we excited? Continue reading
KantanMT recently published a brand-new 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 KNOW in order to stay relevant and ahead of their competitors.
Are you as excited about Localization and Machine Translation as we are? Does solving challenging technical issues at work give you a sense of accomplishment? We have just the job for you. KantanMT is expanding and is looking for a Localization Engineer.
KantanMT is the number one provider of custom machine translation services in the world. The Software-as-a-Service platform, KantanMT.com is used by some of the world’s largest enterprises to increase translation and localization productivity.
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.
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
For more details on implementing your API solution via the REST interface, please see the full API technical documentation at the following link:
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.
You will be directed to the ‘API Settings’ page. Now click on the ‘Launch API’ button.
A ‘Launch API’ pop-up will now appear on your screen asking you ‘Are you sure you want to launch the API?’ Click ‘OK’.
The ‘API Status’ will now change from ‘offline’ to ‘initialising’, the ‘Launch API’ button will now change to ‘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.
Type the text you wish to translate in the text box and click on the ‘Translate’ button.
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.
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’.
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.
You will now be directed back to the initial screen on the API Settings page.
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 firstname.lastname@example.org.
For more details on the KantanMT API please see the following links and the video below:
It can often be challenging to measure the fluency of your Machine Translation engine, and that’s where automatic metrics become very useful tool for the localization engineer.
BLEU is one of the metrics used in KantanAnalytics for quality evaluation. BLEU Score is quick to use, inexpensive to operate, language independent, and correlates highly with human evaluation. It is the most widely used automated method of determining the quality of machine translation.
This is one of the features provided by Kantan BuildAnalytics to improve an engine’s quality after its initial training .To see other features used by Kantan BuildAnalytics please click on the link below .To get more information about KantanMT and the services we provide please contact our support team at at email@example.com.
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.
January 2015 marks the last month of the Moses Core project. The project started three years ago in 2012, as a collaborative effort by its members to improve translation processes and to create a competitive translation environment. Over those three years, the translation and MT landscape has changed significantly. This change and the project’s success is in no small part due to the hard work and diligence of the Moses Core project coordinator; TAUS and with TAUS’s kind permission, KantanMT is republishing the MT use case for the KantanMT Community.
KantanMT.com is a registered trademark of Xcelerator Machine Translations Ltd.
The platform was launched commercially in Q4 2013, however, we have been rigorously testing KantanMT.com in academic and commercial settings since 2012. In the beginning, the product was offered as a free trial to the KantanMT Community, and their feedback was instrumental in shaping and improving the platform to what it is today.
The Moses technology has improved immensely over the past 12-18 months. Developer documentation and support materials, while initially very basic, have matured into a more structured, comprehensive and helpful resource. Additionally, the management of software distributions has made it easier to work with, understand and deploy. These are key elements in maintaining and supporting any open-source technology and have made Moses a key technology for the localization industry.
The rise of the global economy and the driving demand for multilingual translation created a gap in the market for a sustainable translation method that could automatically scale to accommodate fluctuating translation needs. The KantanMT Development team was able to utilize the open source Moses decoder to develop a cloud-based Statistical Machine Translation (SMT) platform, where clients could build and manage their own customized MT engines without compromizing on the ownership of their data. The flexibility, scalability and security of the Moses toolkit made this possible.
The Moses toolkit offers the most flexibility in implementing an SMT solution for commercial purposes, as it allows the system’s training and decoding process to be modified. This has enabled the KantanMT team to create a high-value product that is dynamic and commercially relevant.
To ensure the product could scale and adapt to user needs the KantanMT team needed a decoder that could be built and managed on the cloud. The Moses system enabled this functionality.
Parallel language data is required to train an SMT engine. This data is an important resource for companies, and current generic SMT engines do not guarantee the security or safeguard the ownership of these assets. In using the Moses decoder, the KantanMT team created a product that could ensure its clients’ data was kept private, and not repurposed or reused in anyway.
Many global companies have large repositories of bilingual data, however, they often do not wish to deploy and maintain their own version of the Moses decoder. The KantanMT Development team was able to develop the sophisticated Moses SMT technology into a package that could be easily accessible to companies wishing to translate their content, and over time achieve localization cost savings.
The current machine translation development team consists of four people, who maintain the platform and build machine translation engines for clients. Due to significant growth in the company over the past year, KantanMT.com will be hiring more staff over the course of the next few months to build engines for clients.
Insource or Outsource Moses/Implementation
Based on research, the demands of the language services industry and enterprise machine translation buyers, KantanMT has implemented and customized the Moses decoder in house to create a robust and commercially viable machine translation product that can scale and adapt to our clients’ needs. The original/base KantanAnalytics™ technology was co-developed with the CNGL Centre for Global Intelligent Content, an academic-industry research Centre based in Dublin City University, Ireland. However, all other KantanMT.com technologies have been developed in house by an in house expert development team.
Number of Engines
As of January 2015, the total number of MT engines built on KantanMT.com by the KantanMT community is 6,777 engines.
As of January 2015, the total number of training words uploaded to the platform by the KantanMT Community has surpassed 50 billion, and the number of translated words on the platform is now more than 600 million.
bmmt GmbH is a German language service provider with a strong focus on machine translation. It needed a Machine Translation provider, which would give the bmmt team full control of their Machine Translation training data and MT engine customization process at a low investment point. They also required a system which could correctly handle format-specific tagging and transparent transfer of mark-up information.
In early 2013, bmmt joined the KantanMT Community and began testing different customization processes using client specific training data. The team initially experienced minor problems with their SDLXLIFF files. However, the KantanMT development team were able to quickly solve this problem by restructuring some of its tokenizers.
The company began deploying production engines in mid-2013. These were showing particularly high Quality Evaluation (QE) scores due to the quality of their training data and resulted in a considerable increase in translation productivity. bmmt MT technicians found that domain specificity is a better basis for predictable output than sheer input size.
bmmt is currently using approximately 20 KantanMT engines in production across technical and automotive domains. These production ready engines are experiencing high quality metric scores for each language combination.
KantanMT.com is one of the market leaders of cloud-based machine translation services. It provides cloud-based SMT services to major global enterprises and software companies wishing to translate large volumes of data. It works directly with companies to develop and implement a long term machine translation strategy, or it works with a select number of language service providers (preferred MT supplier partner program) to supply MT services to large enterprises.
Machine translation is now much more widely accepted in the industry, than it was just a few years ago. Since KantanMT.com entered the market in its testing phase in 2012, we have seen an enormous change in the attitudes and perception of MT in the language community. Access to technology such as smart-phones and tablets in non-English speaking nations has driven the global marketplace, and this in turn has increased the need for on-demand translation services – driving demand for MT services. The MosesCore Project has facilitated this demand with an open source solution that made it possible for smaller companies, and startups like us to compete against bigger MT providers, to solve the problem of language.
“The KantanMT platform sets a new industry benchmark in terms of analytics and development tools used to build and measure the quality of Statistical MT Engines. The KantanMT expert development team has introduced some of the industry’s most exciting and valuable technologies built on the Moses decoder, which are helping language and enterprise clients to translate more efficiently and reduce costs.” KantanMT.com founder and Chief Architect, Tony O’Dowd.