3 types of machine translation

If you are a content manager and own a business, you know how much time writing takes. For instance, you can order one of your writers to write good content for your blog, for example a content-rich article of about 1,000 words. It soon adds to 10,000 words of valuable content that you need to transfer from one language to another. How fast can you expect the work to be done to get Spanish Translations, French Translations or German Translations of those 10,000 words? Well, it can take some days or weeks to get it done, depending on whether you use freelancers or a professional translation company. Anx this is where modern technology steps in. If you need to produce volumes of work in several languages within a limited time span and with a tight budget, machine translation is what you need. With this technology you will save on to two important aspects: money and time, but its deployment must be well planned.

The next few articles in our blog will deal with the importance of planning well your machine translation strategy and incorporating a machine translation workflow and use in your organization, whether you are a translation company or a translation buyer. We will use our experience at Pangeanic as the first LSP in the world to deploy Moses commercially and how we grew from there to create PangeaMT and serve custom engines and full machine translation systems.

If you need to meet strict deadlines and need your work translated quick, human translation services might take up time than expected. Rushing human translators is bound to produce mistakes. Squaring the cost/time/quality triangle and building scalable translation strategies is something that few companies have achieved in international publication. But when it comes to machine translation, and acknowledging that you will get some comprehension errors, you save time. And in language pairs for which it is very difficult to find a translator (imagine translating Japanese into Turkish as some of our clients requested recently), machine translation is the only option when speed is essential. Machine translation is a tool to speed translators’ output so they produce more. Popular online translators have made this possible. However, in real-life scenarios, many clients require special formats, very particular expressions and terminology adherence that generalist engines cannot offer. There are many gains in machine translation, but the main benefits always come from building specific and custom engines using the client’s previously translated material and terminology.

Gone are the days in which only large corporations could afford buying machine translation engines. Pangeanic, via its machine translation division PangeaMT has offered custom-built MT engines for years to companies and to other Language Service Providers, providing them with a key competitive edge and allowing for large projects to be completed on time, fast and efficiently.

At Pangeanic, we speak about the 3 typical uses of types of machine translation we can encounter

  1. for gisting (simply understanding what something says, with little lifetime value and low expectations by the user). Here machine translation engines exist prior to human interaction
  2. for publication (for serious publication work with a higher lifetime value for the document and high quality expectations by the user). Humans are in control of the input with which the engines have been trained and these perform according to their specific needs and domains. Here, machine translation engines are created after human users have decided that it is viable to use MT and they use it for a purpose.
  3. for human interaction (when humans do not speak each other’s language and a voice recognition software converts speech to text which is then machine translated and converted again into speech).
3 types of machine translation: understanding, publication, human interaction

3 types of machine translation: understanding, publication, human interaction

 

Everyone has used free online translation systems. The users’ approach to them is that it should be instantaneous, fast and free. And it should cover as many language areas as possible. In other words, it should be like a sheet: good length but not too much depth. Lower quality or unreliable outputs are acceptable as the service is free.

The second case is what concerns translation professionals and it is the use of custom-built machine translation engines for a specific purpose. Typically, translation professionals will pay for this service as a professional service and tool with its own ROI as it will lead to higher outputs by professional translators who save time in typing, reading and understanding and sometimes looking up terminology. A well-built translation engine will contain specific terminology that will save invaluable time to post-editors even if they do need to improve the sentence to make it flow and sound human. Post-edited material, constantly evolving techniques in natural language processing, hybridation, etc. This is the area where machine translation has made the highest impact in professional and quality publication, as an aid and tool for translators’ to use.

A spin-off case of the above is the use of customized MT with an API to translate web content on the fly, for example making calls from your content management system to a custom-built engine that can serve fast translation of products, short reviews, etc. Opening this type of access to machine translation can open new revenue streams to companies as they can add new services to their clients with the right technological partner.

As explained above, the third application of machine translation engines is human to human when people do not speak each other’s language. Some claims have been made about speech-to-speech translation lately, but mostly in controlled environments. Let us remember that although speech recognition has advanced a lot, there is a training time required for the software to recognize one’s tone and some accents are better recognized than others. Without prior training, speech recognition can fail. This is a loss to which we have to apply machine translation and convert text to speech again.

Stay tune to our blog to find out more about Pangeanic’s applied machine translation strategies and how our technology has provided success stories to both larger translation companies, organizations and companies in a variety of sectors.

2 thoughts on “3 types of machine translation

  1. Andres Sahuquillo

    I hadn’t thought of machine translation being divided in 3 uses as you propose. It makes sense that humans (writers, as in my case) are involved or not involved in the first two cases you mention.
    I read recently about Microsoft’s enabling life conversations to be translated on the fly http://research.microsoft.com/en-us/news/features/translator-052714.aspx and wonder how accurate this could be or if it is just a lab experiment. It sounds too sophisticated to be true.

    I have had experiences with dictation software and I am amazed how accurate it can be. Why not add machine translation to speech recognition to create the ultimate “life translator”?

    Reply
  2. nabdak

    Excuse me, I research too much in this topic and I haven’t find the division you are talking about. Have you any references for this proposal?

    Reply

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