Machine translation (MT) vs. human translation
When you work in the language industry, there is no way you haven’t crossed paths with machine translation one way or another, since it is one of the most dynamically improving areas of the translation industry. MT engines are continuously getting trained and becoming more accurate, thus making the translation processes (in selected areas and topics) smoother and more cost-effective.
Most MT engines use neural networks (hence the name neural MT engine), similar to those found in the human brain, to construct sentences in the target language. On average, machine translation can nearly reach the level of human translation and, in certain contexts and genres, the translation quality can be very high.
But what does this all mean to translators? Will machine translation replace humans in the future? Where is the industry headed?
The future of machine translation
According to memoQ’s expert team, there are two major areas where machine translation engines are expected to improve in the near future.
One of them is the edit score (the percentage of the workload that a human translator faces when post-editing a machine-translated text). According to calculations, the edit score is now around 20-40%. We expect that with the continuous development of machine translation systems, less and less editing may be required in the future which will reduce the overall edit score.
The other area where MT can improve significantly is in speech-to-text translation. Translation does not stop at text-to-text translation, but that speech recognition and speech-to-text translation will gain more and more visibility in the localization industry.
Machine translation and translation management systems (TMS)
Let’s also look at the opportunities within a TMS. When going about your translation job, your client can often require you to use certain terms. In that case, we recommend that you either use a glossary, or enhance your matches coming from MT with term base entries.
When it comes to machine translation post-editing, we believe that the best translation quality can be achieved by using both a machine translation integration and your translation memories or term bases.
Another thing that can be anticipated is that more minority languages will be supported by the algorithms. For languages that have fewer speakers, machine translation results are not always as accurate as when you translate from/to a more widely spoken language.
We believe, however, that in the future, machine translation will become more inclusive, and the support of lesser-known languages will likely improve, thus making post-editing efforts more seamless and less troublesome.
English as a lingua franca…?
One common hypothesis we see across the industry is that learning English (or languages in general) will not be needed in the future because machine translation quality will improve so much that it creates perfect translation output. MT will be able to perform the translations needed for people to communicate.
However, this is probably not the case. After all, computers were designed to approach problems from a mathematical point of view. They are able to take a sentence from the source language and put it into a target language.
But language and communication (and thus, translation) has so many more aspects. Computers will never be able to understand context and culture on the same level as humans. Computers cannot use wordplay, puns, jokes, or play around with syntax to convey a different meaning or put the emphasis on a different part of the sentence. They cannot express things using different writing styles, and they cannot tailor content to the genre and the tone of voice.
It’s hard to tell what else is going to happen to the language industry in the future, but one thing is for sure. Localization management and machine translation will surely be intertwined and complement each other. To learn more about how you can take advantage of MT in your translation workflow, head over to memoQ’s complete guide on machine translation.
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