Over the past decade Machine Translation has evolved at an enormous pace and the introduction of (Deep) Neural Machine Translation as well as context aware systems (as opposed to segment or sentence-based systems) present us with a wide choice of suppliers and options for almost any major language and domain that offer very good quality. The “right” engine applied to a specific domain and language pair typically provides such good results that humans often find it impossible separating machine translation from human translation and localization productivity is between 30 – 60% higher than without machine translation. Due of these developments our approach to Machine Translation in localization (which has not changed substantially the past two decades) requires a rethink.
Given the breadth of suppliers and quality of services available in the market, the first and primary challenge for anyone aiming to apply machine translation in localization projects effectively is not to find just an engine or supplier, but the best suited engine for the project. The post-editing efforts between different engines for a given language pair can differ substantially leading to differences in efforts between 100-300% between different types of engines for the same language pair. With a better engine to fit your content, translating using a better system directly improves your bottom line.
Machine Translation Gateway
Finding the best fit is of essential importance – not a simple feat given the wide variety of suppliers and good quality engines in the market these days. That’s where tools such as the CrossLang Machine Translation Gateway with its access to all major suppliers including custom engines comes into play. Not only does such a tool offer access to thousands of engines in the market though a single interface, but it also offers a simple way to filter down choices based upon language pair, domain, capabilities such as the ability to override terminology but also GDPR compliance and other key capabilities.
One of the most important capabilities, however, is the ability to, through an automated engine advisory, provide you with a qualified view of the best engine for your project – helping you to find the best suited engine in a haystack of 100’s of engines and quantifying the savings in clear numbers.
Rising quality level of Machine Translation
Having accepted the fact that machine translation has achieved such quality, however, through leaders such as ESTEAM are even starting to question the technology stack and processes as we know it, suggesting that quality machine translation systems can replace translation management systems (TMS) altogether and provide a range of additional benefits by “understanding” the structure of your content.
While such propositions may sound outlandish to many industry veterans, there is a clear logic and merit to the approach – if the engine is either a very close fit to your source or trained specifically for it. What their successful implementation in a wide range of production environments however does prove is that machine translation quality has reached quality levels that for many domains and applications reach very close to human parity.
Bottom line: it is time to rethink our approach to machine translation. While the machine translation first approach may be a bridge to far for most customers, it goes to prove that if you are not using machine translation effectively today you are likely overspending and keeping an eye on new approaches is the wise thing to do.