This week, Dr Dimitar Shterionov, Machine Translation Researcher at KantanMT, presented at the Cloud Security workshop conducted by Irish Centre for Cloud Computing and Commerce (IC4). The information-packed workshop, which was a huge success, aimed to draw back the curtain on cloud security and help companies make more informed choices regarding cloud security within their organisation.
In this post we will highlight some of the issues discussed during the workshop as well as the best practices, tools and guidelines that will help decision making for businesses making the move to the cloud.
KantanMT has an ongoing Academic Partnership with Centre for Multidisciplinary and Intercultural Inquiry (CMII) at University College London to accelerate research and learning in the field of Machine Translation (MT). The postgraduate students of the department were able to use the KantanMT platform to update or gain new skills in Translation Technology. With help of the KantanMT platform, the students learnt how to build and customise their own Statistical Machine Translation (SMT) systems in a real world scenario.
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.
Do you have what it takes to provide outstanding customer service? Are you excited about Localization and Machine Translation? We have just the job for you. KantanMT is expanding and is looking for a Customer Service Engineer.
The ideal candidate should be enthusiastic about helping our customers who are using the Machine Translation platform, have patience, and should be a self-starter.
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 customisedmachine 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 email@example.com and we will take care of the rest.
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Master’s student, Rafaella Athanasiadi of the University College London submitted her thesis as part of the MSc degree in Scientific, Technical and Medical Translation with Translation Technology. Rafaella was supervised by Teaching Fellow and Lecturer Dr. EmmanouelaPatiniotaki and she used KantanMT.com for her research. This guest blog post looks at some of her conclusions on Machine Translation and the Localization Industry.
As Hutchins & Somers (c1992:1) argue, “the mechanization of translation has been one of humanity’s oldest dreams.” During the 20th century, the translation process changed radically. From spending endless hours in libraries to find the translation of a word, the translator has been placed in the centre of dozens of assistive tools. To name just a few, today, there are many translation software, terminology extraction tools, project management components, and machine translation systems, which translators have the opportunity to choose from while translating.
However, shifting the focus to audiovisual translation, it can be observed that not so many radical changes took place in that area, at least not until the introduction of machine translation systems in various projects (such as, the MUSA and the SUMAT project) that developed machine translation engines to optimise the subtitling process. Still, the results of such projects do not seem to be satisfactory enough to inspire confidence for the implementation of these engines in the subtitling process both by subtitling software developers and subtitlers.
Based on my personal research that focused primarily on the European setting, in the subtitling industry it seems that only freeware SRT Translator incorporates machine translation while also offering the features that subtitling software usually incorporate (i.e. uploading multimedia files and timecoding subtitles) at the moment. Nonetheless, SRT Translator, which is not very famous among subtitlers, uses solely Google Translator by default, which is a general-domain machine translation engine and not suitable for the purposes of audiovisual translation, one could argue. The quality of the output of Google Translator was tested by translating 35 subtitles of a comedy series. The output was incomprehensible and misleading in many cases.
Even though no further records of traditional subtitling software that incorporate machine translation could be found, there are many online translation platforms that allow users to upload and translate subtitles. Taking into consideration the European market, these can be either translation software like MemoQ, SDL Trados Studio and Wordfast that offer thability to load subtitle files and in some cases link them to the audiovisual content they are connected to, open source tools for translators like Google Translator Toolkit (GTT) or professional and private platforms like Transifex and XTM International that are used by companies and offered to their dedicated network of translators. Nonetheless, in order to enable machine translation in all the above applications, API keys must be purchased. GTT is an exception since it can be used for free anytime and only requires a Gmail account.
The fact that subscription fees have to be paid along with the costs of API keys for each machine translation engine provider puts their usability in question since costs may overweight subtitlers’ profits. Furthermore, these platforms cannot accommodate subtitlers’ needs; for instance, the option to upload and play multimedia files while translating the subtitles is not always possible nor any synchronization features for timecoding the subtitles to the audio track are offered. Transifex, however, is an exception since this localization platform offers users the option to upload multimedia files in the translation editor while translating the subtitles.
According to Macklovitch (2000:1) a translation memory is considered to be “a particular type of translation support tool that maintains a database of source and target language sentence pairs, and automatically retrieves the translation of those sentences in a new text which occur in the database.” Even though machine translation engines were developed through different projects to reduce subtitling time to the least possible degree, no attempts had been traced during this research to integrate a translation memory tool in a subtitling software for optimizing subtitling; at least in a European, Asian and Australian setting. As Smith (2013) argues, “traditionally subtitling has fallen outside the scope of translation memory packages, perhaps as it was thought to be too creative a process to benefit from the features such software offers.” However, as Diaz-Cintas (2015:638) discusses “DVD bonus material, scientific and technical documentaries, edutainment programmes, and corporate videos tend to contain the high level of lexical repetition that makes it worthwhile for translation companies to employ assisted translation and memory tools in the subtitling process.”
Even if such tools have not been integrated in subtitling software, translation memory components are used for subtitling purposes in cloud-based platforms such as GTT, Transifex and XTM International as well as in translation software, MemoQ, SDL Trados Studio, Wordfast Pro and Transit NXT by simply creating a translation memory before or while translating. It should be noted that Transit NXT is the only translation software that can accommodate the needs of subtitlers to a high level among the tools discussed in this research. Apart from the addition of specialized filters to load subtitles (that also exist in MemoQ, SDL Trados Studio and Wordfast Pro), subtitlers can upload multimedia files, translate subtitles while a translation memory component is active and also synchronise their subtitles with the Transit translation editor (Smith, 2013).
Figure 1: The translation editor of Transit NXT by Smith (2013)
The newly-founded company (2012) OOONA has taken a very interesting approach to subtitling by developing a unique cloud-based toolkit that is built exclusively for accommodating the needs of subtitlers. When asked the following question within the context of the MSc thesis,
Considering that other cloud-based translation platforms like GTT, Transifex and XTM International offer the option of uploading a TM or a terminology management component, do you think that it is important to offer it on a subtitling platform as well?
the representative of OOONA (Alex Yoffe) replied that not only will the company implement translation memory and terminology management components in the next phase of enhancing their platform but that they also consider these components to be very important for the subtitling process. In addition, Yoffe (2015) argued that OOONA intends to “add the option of using MT engines. Translators will be able to choose between Microsoft’s, Google’s, or customisable MT engines.” Therefore, it seems that OOONA will become a very powerful tool in the near future with features that will optimise the subtitling process to the maximum and shape the way that subtitling is carried out until now. The fact that Screen Systems, Cavena and EZTitles have partnered with OOONA is an indicator of how much potential there is in this toolkit.
As it can been argued based on the above, there is lack of subtitling software with incorporated translation memory tools. Therefore, this issue was further researched through the form of an online questionnaire that was disseminated to subtitling companies and freelance subtitlers. In addition, two companies that develop subtitling software, Screen Subtitling Systems and EZTitles, were asked to present their views on this topic. In both cases, their willingness to optimise the subtitling process in a semi-automated or a fully-automated way was apparent through their answers. The former company was in favour of a combination of machine translation tools with translation memory tools whereas the latter leaned towards a subtitling system with integrated translation memory and terminology management tools.
Nonetheless, the optimisation of the subtitling process has to coincide with the needs and preferences of subtitlers. Based on the respondents’ answers, it is clear that translation memory tools in subtitling software are desirable by subtitlers. In question,
Which tool would you prefer to have in a subtitling software? An integrated translation memory (TM) or machine translation (MT)?
more than half of the respondents (56.8%) chose TM. Interestingly, the answer Both received the second highest percentage (20.5%) which indicated that subtitlers demand as many assistive tools as possible.
One of the main conclusions that were drawn from this research was that machine translation engines need to be customised to produce good quality output and this can be achieved through customisable engines like KantanMT and Milengo. Moreover, translation memory tools are sought by subtitlers in subtitling software, while cloud-based platforms seem to occupy the translation industry today. Following this trend, subtitling software providers partner with online services/tools like the OOONA toolkit.
Based on the outcomes of this research, it could be said that we are certainly experiencing a new era in subtitling since the traditional PC-based subtitling software are now transforming into flexible and accessible platforms to enhance the subtitling experience as much as possible. It is a matter of time which tool and platform will rule the subtitling industry but one thing is for sure; the technologies of the future will bring a lot of changes in the traditional way of subtitling.
Diaz-Cintas, J., 2015. Technological Strides in Subtitling. In: S. Chan, ed. Routledge Encyclopedia of Translation Technology. London: Routledge, pp. 632-643.
Hutchins, J. W. & Somers, H. L. (c1992). An introduction to machine translation. London: Academic Press.
Macklovitch, E. (2000). Two Types of Translation Memory. In Proceedings of the ASLIB Conference on Translating and the Computer (Vol. 22).
If you are in the language service industry, you are undoubtedly on the lookout for ways in which you can improve the productivity of your team – more translated words in less time – that’s what drives your clients as well as you. Automated Machine Translation (MT) seems to be the logical step forward in today’s world of content explosion and tightening deadlines. However, for most Language Service Providers (LSPs), the challenge lies in the actual implementation of this sophisticated technology.
For this reason, it is important that no matter what translation management tools you use, it should be integrated with a powerful MT engine that is reliable, scalable, flexible, and can be trained and re-trained constantly for maximum efficiency and quick turnaround times.
In today’s fast-paced world of content explosion on the Internet, the need for translating this organically growing content with the help of machines has become inevitable. While post-editing the machine translated content will always be required, choosing the right MT features will ensure that translators do not spend countless frustrating hours on those edits.
In this Kantanwebinar, The KantanMT Professional Services Team, Tony O’Dowd and Louise Faherty (Quinn) will show how you can improve the translation productivity of your team, and manage effort estimations and project deadlines better with a powerful MT engine.
During this webinar you will learn:
Translation challenges (co-ordinating and managing translation projects)
About the necessity of Machine Translation to be competitive
How KantanMT.com can be integrated with other Translation Management Systems