Student Speak: Using MT for Game Localization

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Giulia Mattoni, an Italian Translation Technology student from DCU talks about her experience using Machine Translation for evaluating player support content localization. Giulia’s fascinating view illustrates why this area needs further research, and how she used KantanMT to evaluate MT and post-editing for this type content. Continue reading

5 Questions with Riccardo Superbo

Riccardo Superbo KantanMT

Welcome to our second post in the ‘5 Questions’ series, which will give you a deeper insight into the people at KantanMT.

Last week, we introduced Laura Casanellas who aced the 5 questions. This week we will introduce you to Riccardo Superbo, who is recently back from a long and fulfilling Trans-Mongolian journey.

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Student Speak: First Time Using Machine Translation

Elodie Vermant, a Swansea University student, studying for an MA in Professional Translation, shares her experience on using Machine Translation for the first time at Swansea University.

The MLTM11 Translation Technologies Module is taught by Dr. Maria Fernandez-Parra, Lecturer, Languages, Translation and Communication at Swansea University. Read more experiences from her students.

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Register for the 1st International Summer School in Translation Technology; 29 August – 2 September, 2016

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The University of Leuven (KU Leuven) is organising the 1st International Translation Technology Summer School for language professionals who are looking for a practice-oriented and state-of-the-art introduction to translation and localization issues and tools, from 29 August to 2 September, 2016 at Campus Sint-Andries, Antwerp, Belgium.

Antwerp_town_hallThe registration deadline is 31 July and there are a limited number of places still available.  The summer school, developed in collaboration with industry experts and consultants, translators’ association from Belgium, and guest lecturers from renowned universities, aims to help the participants make informed decisions when switching to modern translation environment systems.

The programme of hands-on workshops and lectures and it is suitable both for young graduates and language professionals (Translators, Project Managers, Translation Technology Lecturers) who are looking for a practice-oriented introduction to translation and localization issues and tools. Continue reading

University Speak: Automated Translation with Professor Cristina Toledo Báez from University of Alcalá

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Professor Cristina Toledo Báez

KantanMT’s  Academic Partner the University of Alcalá (UAH), used  the KantanMT platform to teach final year undergraduate students about Machine Translation during the 2015-2016 academic year.

KantanMT.com was used in the course ‘Machine Translation and Post-editing‘, which was taught for the first time in the ‘Degree in Modern Languages Applied to Translation’ in UAH. English and Spanish were used as the main languages used during this course.

We caught up with Professor Cristina Toledo Báez, and in this post she describes her experience of using KantanMT during the course.

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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.

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

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

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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.

 

Get that Customer – Why Machine Translation Works Best for eCommerce Product Descriptions

KantanMT for eCommerceIt’s a fact, infiltrating new markets is the key to increasing profits, and the first item on any company’s internationalization checklist should be to make sure it communicates product information in a way its target customers can understand.

In 2006, Common Sense Advisory (CSA) identified that 73% of consumers read content in their native languages, and 42% prefer to make purchases in their native tongue (Can’t read, won’t buy).

Leading on from the 2006 research, CSA’s updated survey in 2014 was based on a sample of three thousand global respondents, and it reinforced earlier results by showing that 55% only buy from websites in their native language. This jumped dramatically to 80% in cases where the buyers English language ability is limited.

When it comes to selling internationally, tapping into new revenue streams demands translated content. But, what happens when you have thousands of product descriptions that need to be localized into a plethora of languages?

This is where the fun begins for localization teams with well-established traditional translation workflows in place. Their existing method seems fine…but when it’s time to scale up, this is when cracks in the process begin to appear.

The translation workflow works best when it matches the scale and velocity for the content created whether it is product descriptions, manuals or online help documentation.

The challenging part –

How to translate product descriptions with velocity and to scale?

We have heard a great deal of arguments for and against machine translation and one of the most well known against arguments is “the quality is rubbish, sentences translated by machine translation are garbled and incomprehensible”. We in the language technology field hear this frequently and often shudder in disbelief at how these conclusions have been reached.

Generic or free machine translation systems in most cases do not produce great results, expecting such a system to produce publishable quality MT results or using it as benchmark for all MT systems is akin to extracting blood from a stone. Achieving good MT output takes time, care and the ability to customise the MT system properly.

Any company that is serious about breaking into international markets should also be serious about their MT strategy. They should be considering a customised MT solution that is tailored to their needs, not just by going for a cheap and/or supposedly free option.

KantanMT- Machine Translation for product descriptions

Why is MT customisation so important?

Statistical machine translation is based on machine learning and pattern recognition. Segments with multiple word phrases or n-grams as they are known are identified with probability algorithms that select the most probable translation match. Generic or free MT systems typically have been built on a broad mix of content styles and types. This means it’s much harder for the MT system to identify the most likely or even relevant matches in generically built engines.

When the MT system is customised specifically for content that comes from a single domain, such as product descriptions for a specific categories e.g. Home and garden, fashion or electronic devices, the syntax, style and phraseology used will make sure that when an MT match is generated there will be a higher probability that the match will be closer to the desired output, resulting in a much more accurate translation.

How important is saving costs?

Of Course Machine Translation can save costs – if done properly, significant savings can be made. But, saving costs is often not the end goal for implementing a serious MT strategy. The real gains come from increasing productivity without a compromise in quality. Why translate 2000 words a day when you can machine translate and post-edit 8000 words with no loss of quality? Really it can be done! See an example first hand (Netthandelen’s case study PDF download).

When it comes to eCommerce and selling hundreds of products online the words to be translated are counted in billions not thousands, and without MT, traditional localization budgets would become more and more expensive, so MT is really the only practical solution. But, if MT is considered a way to save money by cutting corners then it is doomed to fail from the outset.

It will fail because it’s not sustainable, the effort and costs required to fix bad quality MT output are too great, and if fixing is neglected by publishing the content as is, it will result in angry customers who shop elsewhere – and they will, as the choice available now is greater than ever before!

KantanMT Key Takeaway Key takeaways

  1. Generic free MT will not generate the same quality as customised MT
  2. Investing in a robust MT strategy will save time, costs and headaches in the long run
  3. Keep focus on communicating with the customer, in their language and your eCommerce business will thrive

Email louisei@kantanmt.com if you have questions or want to learn more about how Machine Translation works for product descriptions.