More Questions Answered on How MT Helps Improve Translation Productivity (Part II)

Part II

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.

KantanMT_ProductivityQuestion: 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 demo@kantanmt.com and we will take care of the rest.

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Applications Of Machine Translation And Translation Memory Tools In Audiovisual Translation: A New Era?

Rafaella Athanasiadi, UCL KantanMTMaster’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. Emmanouela Patiniotaki 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.

Rafaella Athanasiadi, KantanMT UCLBased 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[1]. 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.

KantanMT Machine TranslationAccording 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).

The translation editor of Transit NXT by Smith
The translation editor of Transit NXT by 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.

Works Cited

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).

Smith, Steve (2013). New Subtitling Feature in Transit NXT. November 11 2013. [Online]. Available from: http://www.star-uk.co.uk/blog/subtitling/working-with-subtitles-in-transit-nxt/. [Accessed 01 Sept. 2015].

Yoffe, A (2015). MT and TM tools in subtitling. [Interview]. 13 August 2015.

[1] Relevant data are available in Appendix 1 of the MSc thesis.

 

University Speak: The Future of Translation Technology with Swansea University

A few months back we were pleased to announce our Academic Partnership with Swansea University’s Department of Languages, Translation and Communication. 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.

The MLTM11 Translation Technologies Module aimed to broaden and deepen students’ familiarity and expertise with current translation technologies. A number of students in the programme were interested in careers in the localization industry. Interests and aspirations ranged from working as an in-house IT specialist and/or localizer, a developer and tester with a major translation tools company, or working as an academic researcher (e.g. a PhD) in the field of language technology.

Now that the course is complete and the students are ready to contribute to the world of Machine Translation, we asked them a few questions about their experience of using the platform. We also asked the lecturer from the course to tell us a little bit about her experience of working with us. Here’s a few bites from each of our brilliant KantanMT users.

Dr. Maria Fernandez-Parra, Swansea University

LecturerDr. Maria Fernandez-Parra, Languages, Translation and Communication

About Dr Fernandez-Parra:

Dr Fernandez-Parra is interested in all aspects of translation and interpreting, particularly translation technologies, translation theory, technical and specialised translation and computer-assisted translation. She is also interested in many aspects of linguistics (especially formulaic language) as well as in the teaching of Spanish in Higher Education. One of her more recent interests is the introduction of technology into teaching in Higher Education.

What does Dr Fernandez-Parra think about KantanMT?

KantanMT is very useful for teaching purposes for various reasons but one reason that stands out is that it allows lecturers to show students an example of how to train data for MT output. This is not possible with other MT tools that I know of. Students can experiment with the creation of training data for their specific source texts, as opposed to general training data that might not be too relevant to some source texts. This allows them to get the best results possible when translating with MT and it gets students familiar with the concept of training MT engines on specific datasets.

It is good that students can start learning about this concept and have some practice and experience in it, as we know there are increasingly more career opportunities in this field of work. Another advantage of KantantMT is that it is very convenient to access it online for teaching purposes, as we do not have to worry about installing anything in our university server and there are no licensing problems involved. KantanMT staff were very helpful and quick to respond to queries. The use of KantanMT online is easy and intuitive and it does not take long for students to be working with their own training data in KantanMT. In addition, there is a wide range of useful information on the web site and a variety of useful resources too.

Student: Ms Min Luo

I think CAT Tools will become more and more helpful in China.

KantanMT: What made you choose this course, and how to do you think it will help your career?

Ms Luo: I think this course is useful. It will help the translator when they translate texts containing repetition.

KantanMT: What are your thoughts on using the KantanMT platform, what did you find good and do you have any suggestions for improvement?

Ms Luo: It is good. It is convenient. I have no suggestion.

KantanMT: What is your impression of the translation industry, and in your opinion, what do you think the industry will look like in the future?

Ms Luo: Technology changes quickly. I think CAT Tools will become more and more helpful in China.

Student: Ms Gwladys Petitfrère

KantanMT.com is very different from other MT I have used before. It is very easy to use and gives great results.

KantanMT: What made you choose this course, and how to do you think it will help your career?

Ms Petitfrère: I wanted to learn how to use various CAT tools for my career as a translator, if I needed it in the future.

KantanMT: Did you have any previous experience of Machine Translation, if so how does KantanMT.com compare?

Ms Petitfrère: KantanMT.com is very different than other MT I have used before. It is very easy to use and gives great results.

KantanMT: What are your thoughts on using the KantanMT platform, what did you find good and do you have any suggestions for improvement?

Ms Petitfrère: There is a lot of information available about every possible specificity of this tool.

KantanMT: What is your impression of the translation industry, and in your opinion, what do you think the industry will look like in the future?

Ms Petitfrère: I think CAT tools are very helpful for the translator, but hopefully we won’t act as proof-readers of the text produced by a machine.

Student: Ms So Mui Cheung

I see contradictory directions. On the one hand, MT is inevitable, welcoming and the way forward.

KantanMT: What made you choose this course, and how to do you think it will help your career?

Ms Cheung: I chose this course because firstly, it offers a PG certificate option and as I already have a Masters degree connected with the language I work with, so I didn’t want to complete another Masters degree.

Secondly, from the point of view of content, I needed a course that focuses on the technology for translation work, and lots of practical hands-on work using the tools. This course meets these criteria. The course sets a direction for a new way of approaching translation. It reflects the rapid changes happening in the field.

Question: Have you had you any previous experience of Machine Translation, if yes, how did you find using the KantanMT system?

Ms Cheung: Most people who use the internet now will have some experience of using MT if only to get a gist of internet content instantly. My comment here is based on very little interaction with the KantanMT platform. It is a tool aimed not at individuals/freelance translators but corporations/language services providers. As such, unless one is engaged to do quite a lot of work or on a regular basis, then the KantanMT platform is not one where one can get the most out of in a short time, as indeed an individual translator is unlikely to have access to it.

KantanMT: What are your thoughts on using the KantanMT platform, what did you find good and do you have any suggestions for improvement?

Ms Cheung: See my comment above – as an individual translator, I can only suggest improvements after having tested the different features thoroughly. I would welcome another chance to answer this question.

KantanMT: What is your impression of the translation industry, and in your opinion, what do you think the industry will look like in the future?

Ms Cheung: Firstly, in the translation industry, at least in the language pair of Chinese>English that I work with, good quality and relevant TMs for training the engine might be a problem. However, in the future – again, in the language pair I work with, I see contradictory directions. On the one hand, MT is inevitable, welcoming and the way forward. On the other hand, depending on the nature of the work, corporations subscribing to cloud-based MT platforms such as Kantan will have issues with confidentiality, and for this reason, I expect some adaption before a cloud-based platform is a widely or readily available tool on an LSP-freelancer basis in the foreseeable future. In the meantime, I expect to see an increase in MT text for post-editing whether online or offline.

Note from the KantanMT team:

Read more about courses offered by Swansea University, or for more information on the KantanMT University Partner Program, please contact us, (info@kantanmt.com).

About Swansea University

Swansea is a top-30 UK research university (Research Excellence Framework 2014), with a record of teaching translation tools and technologies going back to 1999. Its MA in Professional Translation (formerly MA in Translation with Language Technology) has been a member of the European Master’s in Translation Network since its inception in 2009. It also offers a vocational MA in Translation and Interpreting, and a thriving PhD programme in Translation (including recent successful projects on translation tools).

The MT Industry is Evolving: At KantanMT, we are Growing too!

KantanMT The rapidly evolving, dynamic marketplace today has created an enormous spike in the demand for Machine Translation (MT) in a number of industries. According to a new study by Grand View Research, the global Machine Translation market is expected to reach USD 983.3 million by 2022. This is a huge leap from 2014, when the MT market was valued at USD 331.7 million, and this growth projection mirrors a trend in the market. Thanks to globalization, there is an increased demand for cost efficiency in translation, but the amount of linguistic knowledge and time required for translating all the content for a particular business exceeds the capacity of human translation alone.

Key Insights into the Machine Translation Market

Some of the key insights about Machine Translation that the study discusses are summed up here:

  • Statistical Machine Translation (SMT) is a clear winner over Rules Based Machine Translation (RBMT), when it comes to the present market requirement.
  • Globalization and the need to address diverse cultural groups has led to the popularity of translation technology in Asia Pacific, thus opening up new potential markets for MT providers.
  • Proliferation of smartphones and increasing internet penetration is expected to drive MT market growth.
  • Machine translation as a service (MTSaaS) makes use of SMT and is accessible via the web. This allows users to customise their MT engines with their own Translation Memories (TMs).

What this means is that, deploying an integrated MT solution will become a critical success factor for gaining market share in the future.

Potential Challenges

The study reveals major challenges for the MT industry, which includes a lack of quality translations and Quality Estimation (QE) and competition from free translation service providers. Needless to say, a well-rounded back-end knowledge base, along with efficient NLP (Natural Language Processing) capabilities and a scalable model are critical to gaining competitive advantage in the market. The MT providers need to go above and beyond their role as simply providing machine translation services; they need to become solution providers.

How is KantanMT contributing to the MT market?

The KantanMT platform offers massive competitive advantage, not only because we were one of the first entrants in the MT market, but also because thanks to our strategic market insights, we have already identified most of these challenges and developed solutions to address them. As solution providers, we use an intuitive approach that can be summed up in a few words: speed, scalability, simplicity, and security.

Speed

In a market where new products and innumerable variants of those products are being developed almost every day, it is important to have on-demand translated content ready to be deployed. KantanMT helps its clients have the first leap advantage over their competitors by translating content on the fly.

KantanMT engines have the capacity to translate 114 million words in a single day and as of 7 September, 2015, we have exceeded 2 billion translated words, with 1 billion words being translated in the last two months itself.

KantanMT.com User stats September 2015
KantanMT Platform statistics as of 8th September 2015

Scalability

As a business trying to make its mark in the global MT market, it is extremely important to have a solution that has limitless scalable potential. KantanMT engines with scaling technologies such as the KantanAutoScale are devised to ensure that no matter how sudden the spike in content is, the quality and volumes of translated content will never suffer.

The power of KantanMT’s engine was summed up Tony O’Dowd, Founder and Chief Architect of KantanMT.com,

“We are only starting to see the potential growth of the Machine Translation market, and I doubt any other player can operate at this scale as flawlessly.”

Simplicity

Simplicity is at the very core of KantanMT. The company name itself is derived from the Japanese word for simplicity 簡単 (かんたん). KantanMT strives to take the complexity out of the user interface, while powerful MT engines do all the hard work in the back end. Easy to understand analytics can be generated through the KantanMT engines to gather insights into improving engine quality and maintaining translation quality.

Security

Cloud based MT solutions have become the industry norm. However, security concerns are high – especially, if you are in the eCommerce industry or deal with legal information. KantanMT’s multilayered security approach protects and monitors translations ensuring all industry secrets are safe. Unlike a number of open source translating tools, you own the source as well as translated words.

Final words

One of the key findings of the Grand View Research review points out that “strategic joint ventures, coupled with mergers and acquisitions, (which) have been among the key strategies adopted” by major players in the Machine Translation industry. KantanMT recognises the importance of both industry and academic relationships in building a complete MT ecosystem.

With KantanAPI integration, powerful KantanMT engines can be plugged into Translation Management workflows with ease. Our Developer Partners include companies like Kilgray, Memsource and Alchemy Software Development.

We work with a number of Language Service Providers, and some of our Preferred MT Suppliers include Milengo, MediaLocate and more recently Turkish LSP, Transistent. We are proud to have a strong relationship with leading Industry Associations such as the ADAPT Centre, TAUS and EAMT among others. Moreover, our University Partner Program aims to further education in Machine Translation for new industry graduates.

Contact the KantanMT Team, info@kantanmt.com to learn more about the KantanMT platform.

Global Localization Reaches Another Milestone

KantanMT Cloud Machine TranslationAs 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

Create, Test and Deploy Post-Editing Automation Rules with KantanMT PEX Rule Editor

The KantanPEX Rule Editor enables members of KantanMT reduce the amount of manual post-editing required for a particular translation by creating, testing and deploying post-editing automation rules on their Machine Translation engines (client profiles).

The editor allows users to evaluate the output of a PEX (Post-Editing Automation) rule on a sample of translated content without needing to upload it to a client profile and run translation jobs. Users can enter up to three pairs of search and replace rules, which will be run in descending order on your content.

How to use the KantanMT PEX Rule Editor

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

You will be directed to the ‘Client Profiles’ tab in the ‘My Client Profiles’ page.  The last profile you were working on will be ‘Active’ and marked in bold.

Active Profile, KantanMT, Client Profile

To use the ‘PEX-Rule Editor’ with a profile other than the ‘Active’ profile, click on the  new profile name to select that profile for use with the ‘Kantan PEX-Rule editor’.

Then click the ‘KantanMT’ tab and select ‘PEX Editor’ from the drop-down menu.

Client Profile, KantanMT, PEX Editor

You will be directed to the ‘PEX Editor’ page.

Type the content you wish to test on, in the ‘Test Content’ box.

Test Content, PEX Rule Editor, KantanMT

Type the content you wish to search for in the ‘PEX Search Rules’ box.

PEX Search Rules, KantanMT, PEX Editor

Type what you want the replacement to be in the ‘PEX Replacement Rules’ box and click on the ‘Test PEX Rules’ button to test the PEX-Rules.

PEX Replacement Rules, Pex Editor , KantanMt , Products

The results of your PEX-Rules will now appear in the ‘Output’ box.

Output Content , PEX Rule Editor

Give the rules you have created a name by typing in the ‘Rule Name’ box.

Rule Name, PEX Rule Editor , KantanMT

Select the profile you wish to apply this rule(s) to and then click on the ‘Upload Rule’ button.

Profile and Button, KantanMT , PEX

Additional Information

KantanMT PEX editor helps reduce the amount of manual post-editing required for a particular translation, hence, reducing project turn-around times and costs. For additional information on PEX-RULES and the Kantan PEX-Rule editor please click on the links below. For more details about  KantanMT localization products  and ways of improving work productivity and efficiency please contact us at info@kantanmt.com.

 

Translation Machines in Sci-fi

Richard Brooks, CEO, K International KantanMT
Richard Brooks, CEO, K International

This blog post was written by Richard Brooks. He’s a firm believer that life imitates art, CEO of the UK-based LSP K International, a company specialising in translation services for the legal industry and director of the Association of Language Companies.

Translation Machines in Sci-fi

In science fiction, translation of the potentially infinite number of languages spoken by alien species presents a dilemma. How to deal with communication between interplanetary species without resorting to contrivance, or spending the first twenty minutes of each episode’s dialogue clumsily showing characters learning one another’s diphthongs?

The notion of a ‘universal translator’ emanated from Murray Leinster’s novella First Contact, published in 1945 (and clearly that isn’t the only debt Gene Roddenberry owes to Leinster). It’s a greatly helpful – borderline miraculous, in fact – convention of sci-fi: a technological solution to the language barrier, leaving more time for the actual narrative to unfold in one language, typically English.

With the incredible advancements in technology we’re witnessing at the moment such as Microsoft’s pilots of a Skype Translator and the industry leading work KantanMT is achieving in this area, are we seeing the beginnings of live translation – well ahead of Star Trek’s 22nd century deadline? In the meantime, let’s take a look at five of sci-fi’s finest translation machines, which beat anything real-life technology can offer – for now.

KantanMT Blog, Universal Translator

1. Star Trek: Universal Translator

An important part of Star Trek’s near-utopian vision of the future is the Universal Translator. Translating any language into another even while a person is speaking, this exceptionally handy tool means Starfleet craft in any quadrant of the galaxy can speak to new life and new civilizations without confusion.

Voiced by Star Trek creator Roddenberry’s widow Majel Barrett until her death in 2008, the development of a universal translator was, in the Trek universe, a portent of Earth’s cultures achieving universal peace. It’s difficult to imagine Google Translate having the same impact.

This convenient concept has been often copied, and occasionally parodied: in Futurama, everyone in the universe speaks English, rendering Professor Farnworth’s one successful invention – a translation device – useless, as it merely translates English into the dead language, French!

2. The Hitchhikers’ Guide to the Galaxy: the Babel Fish

Some sci-fi plays with the concept in less serious ways. In Douglas Adams’ H2G2, to help Arthur Dent deal in some small way with anything that goes on around him, inserted into his ear is a Babel Fish, memorably described by the Guide as “small, yellow, leechlike and probably the oddest thing in the universe.”

The science (such as it is) behind the Babel Fish is that it can absorb the frequencies of outside speakers, and a translation is secreted by the fish into the hearer’s brain via his or her ear canal. In a witty reversal of Star Trek’s idealistic Federation, Adams reveals that, by allowing everyone to understand one another, the Babel Fish has actually caused more war than anything else in the universe.

3. Farscape: Translator microbes

In science fiction, as in reality, it is the individual idiosyncrasies of languages which are trickiest to master. When people in the UK from a hundred miles apart may speak different languages, not to mention a range of different dialects and accents, can auditory translation really be so smooth?

One series to acknowledge this is Farscape, where astronaut John Crichton is injected with bacteria-sized ‘translator microbes’, which are injected into – and colonise – his brain. The microbes work to make their host understand any spoken information in any language – except idioms are translated literally. This leads to a great deal of confusion for John, and opportunities for humour for the audience (all jokes are language, after all) – and also perhaps renders these microbes a more realistically-limited translator technology.

4. Doctor Who: The TARDIS’ Translation Circuit

As well as being telepathically linked with the Doctor, and granting the ability to travel to any time or place in history and the future, the TARDIS’ telepathic field is used to automatically translate what the Doctor and any companions hear or read into a language which they can understand.

While wonderfully convenient, the mind-meld involved does mean that the translation circuits won’t actually work when the Doctor is unconscious – not an outright impossibility. Also, because translations are time specific, ancient civilization won’t understand neologisms – and, neatly, the Romans have never heard the word ‘volcano’ – because they’ve not lived to see an eruption.

5. Star Wars: C-3PO

Luke Skywalker is the ultimate sci-fi everyman: he is every bit as much in need of a guide to the universe he finds himself in as the viewing audience are. Reinforcing this are his guides, C-3PO and R2D2, who Luke needs with him – despite their obvious drawbacks as travelling companions – because C-3PO is programmed with millions of languages, everything from Ewok to R2’s bleeps and whistles.

When the franchise returns with The Force Awakens later this year (which most fans will rightly consider the fourth, rather than seventh, Star Wars movie), C-3PO’s translation abilities are sure to make him at least partially useful to have around.

The KantanMT team say a big Thank You to Richard for a very savvy post on translation machines in science fiction.

Richard (@RichardMBrooks) will join Tony O’Dowd, (@TonyODowd1) KantanMT Founder and Chief Architect alongside other Language industry heavyweights at the ATC Annual Conference in the Old Trafford Stadium on 24th and 25th September 2015. Register here to attend the conference. 

KantanMT at ATC Conference

If you want to learn more about Machine Translation, send us email (info@kantanmt.com) with your questions and we will be happy to answer them!