Rise of the Machines?

 

Machine translation is a topic that’s come up a lot recently – not just in conversations with clients curious about cutting-edge translation solutions, but in other aspects of everyday life.  We hear about MT innovations in the media, find it embedded in web browsers and probably come across materials in our day-to-day lives that it has generated.  And practical applications aside, it’s a topic that people want to learn more about because it appeals to something basic in our lives – the urge to communicate; to hear and be heard.

As we’ve discussed in the past here on the ABLE blog, it’s our collective opinion that Machine Translation technologies are not yet mature enough to serve as a reliable translation solution for client-facing or otherwise quality-sensitive materials.  That said, for more informal uses, MT can be a useful and practical tool.

While a number of methodologies exist for MT, two leaders have emerged in terms of reliability and quality.

Rule Based Machine Translation:  RBMT translation systems are built largely using linguistic resources like bilingual dictionaries, grammar libraries and syntactic knowledge-bases to assemble translations.  The tools rely on the syntactic knowledge-base to assemble translations.  One of the primary benefits of RBMT is it can be used in conjunction with client-customized glossaries to enhance the translation’s subjective quality.

Statistical Machine Translation:  SMT translation systems are likewise built using existing bilingual dictionaries and grammar libraries.  Instead of using a set of fixed syntactic rules to translate, however, it relies on statistics and probability to select and apply the best grammatical approach.  The statistical approach is calibrated by referring to corpus, or a body of existing translations.

While both of these approaches have their benefits, a so-called hybrid approach is often taken that relies on a mix of Rules-based and Statistical-based principles to achieve the best results for a given client.

In previous blogs, we’ve discussed the drawbacks to MT when it comes to sensitive materials. That said, there are several uses for existing technologies.

Gist-translation of large bodies of content: It’s not uncommon for clients to have repositories of content that are not in need of perfect-quality translation, but still must be understandable for internal use.  Consider an international law firm with boxes of legal documents totaling over a million words.  Not every word of that material will be directly relevant to a case, but it’s critical to review the entire body of text to identify relevant information.  This first pass is an excellent use for MT.  Once the relevant areas of text have been identified, these can then be sent for human re-translation or editing.

First-pass translation: If source text is of a sufficiently literal and straightforward nature, Machine Translation can be adequate for the translation stage of client-facing materials.  This initial translation pass would then be followed by a human editing and quality assurance pass.  Technical manuals are often suitable for translation.

Ancillary translation:  Existing (and often free) MT solutions can benefit clients in many ways, and social media and collaboration clients may find these to be especially useful.  Since these platforms rely on the ability of users of all linguistic backgrounds to interact and share content they themselves have created, formal human translation is impractical or entirely impossible.  This creates a frustrating challenge –it’s easy and affordable to reach out to new markets by simply translating a site’s User Interface, help and other in-house content through traditional methods; but it’s often not worth it if it simply creates a body of segregated linguistic communities.  However, with embedded, easy-to-use tools like Google Translate at everyone’s fingertips, the ability for end users to connect across linguistic boundaries is just a click away.

If you have questions about how Machine Translation may be right for you, ABLE’s highly-experienced team of experts is here to help.  We look forward to discussing your team’s specific challenges and suggesting a customized solution using one or more of these approaches.

 

 

5 reasons NOT to use Machine Translation

Machine translation (MT) is a computer based technology that allows the user to translate from one language to another without any human input. MT uses HTML or XML strings to extract the content to be translated and replaces each word with its equivalent in the target language.

It is important not to confuse MT with Translation Memory (TM) tools. Translation Memory tools are widely used by professional translators to keep translations consistent between projects.  As opposed to MT, CAT tools are human based technologies and can also be thought of as banks where the translation is saved as the translation is being processed.  It allows the translators to shorten the turnaround time and review existing translations from previous projects in order to maintain the same terminology, style and industry specific jargon (Legal, Pharmaceutical, etc.)

The first web-based MT tool was developed by SYSTRAN in the early 90s.  It offered a simple web interface, where one could enter a few words which would then be automatically translated into the target language – similar to a bilingual dictionary. Over the last few years, Google Translate has emerged as the leader in this field. Google recently announced that its translation tool translates the equivalent of one million books a day.

Considering the growth over the last few years, MT will be a major player in the translation industry in the near future. That’s the reason ABLE Innovations is keeping a very close eye on its development, and contributing to its growth as much as possible.

If you are looking to translate one or two simple words from one language to another, MT can be the way to go in most cases. However here are five reasons NOT to use MT for professional applications:

-A machine will always be a machine: Unlike human translation, MT does not understand the context of the translation.  Is the translation intended for a first grader or a brain-surgeon?  MT will generate the same results regardless of your target audience.

-Content creators invest a considerable amount of time in making their content appeal to their specific audiences. Whether you are translating Shakespeare or the sport section of today’s newspaper MT will generate the same results which could lead to a complete misunderstanding of the translation.

-In any language, an action or an item could be described or written in an almost unlimited number of ways (i.e. car, vehicle, automobile, etc). In some instances only one word would fit the definition. MT is not smart enough to understand that, which may lead to confusion for the intended audience in some cases.

-Even if it appears cheaper up-front, machine translation can end up costing more in the long run.  If you dump a large amount of content, you risk receiving nonsense in return.  The cost of having a professional translation firm resolve quality issues can result in longer overall turnarounds and additional costs.  ABLE Innovations highly encourages all of its clients to consult us before using MT.

-Company image: in today’s fast growing world, content is available to the intended audience instantly, which means that there is no room for error. While some companies think they are saving by using MT, their image could be at risk and that can cost a considerable amount of time and money and restore.

At ABLE Innovations, we are always looking for new technologies to help us shine in the industry and consistently provide outstanding language services to our clients. Although we are following MT’s development very closely, it is simply not yet the magic bullet it may seem.

 

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