Advances in machine translation (MT) mean enterprises now have a sophisticated translation solution in their toolkit that can translate quickly and at scale. Long gone are the days of weird menu translations and Yoda-like results. But given the recent rapid advancements in artificial intelligence and machine learning, companies must navigate how to optimally deploy this productivity-boosting approach alongside human translation. Knowing where and when to use machine translation will ensure translations are cost-effective and fit for purpose. Embracing translation technology and innovation in the right areas is the way to increase engagement and efficiency. Read on to find out the criteria you need to consider when deploying the latest machine translation solutions.
Though you may think language translation technology is a relatively modern phenomenon – after all, computers have only been around since The Babbage Difference Engine back in 1822 – its roots stretch back, all the way to the Arabian peninsula in the 9th century, where one al-Kindi translated ancient Greek mathematics, science and philosophy texts that had been lost to European civilisation, helping spark the Renaissance in the process. He developed various systems based on frequency analysis and statistics, key concepts in MT. Now, AI translation software enables clients to customise according to subject area, such as meteorological reports. This has massively widened MT’s applicability and usefulness.
Machine translation was initially developed in the 1950s, and has since been transformed through continuous advances, diverging into four categories: SMT, NMT, RBMT, and Hybrid Machine Translation. SMT, or statistical machine translation, automatically maps sentences in one language into another, whereas NMT, or Neural Machine Translation, encompasses a neural network that relies on algorithms working together to process highly complex data inputs. RBMT, or Recurrent Batch Machine Translation, replaces the input texts with translations of a set of translations of the same text, and Hybrid Machine Translation combines elements of both NMT and RBMT.
The advantages of using machine translation mean it is a very effective and efficient solution in a company’s toolkit. Firstly, machine translation is incredibly fast. It can process huge volumes of text in a near instant. Therefore, it improves efficiency and productivity. Companies that deploy machine translation typically see an improved profit margin, all else being equal. Secondly MT is scalable. If you need to translate a short document or an entire library’s worth of text, MT can handle it. Lastly, and partly as a result of the first two points, machine translation is much more cost-effective than human translation. Before you rush out to onboard a range of machine translation solutions, there are many circumstances where human translation is preferable, and numerous others where a hybrid approach of machine translations processed by human editors is best.
Getting the best out of machine translation requires optimally deploying it. Several factors will determine the ideal approach. A Nimdzi survey of 33 localisation buyers found 22.6% report extensively using neural machine translation. The survey notes that sectors like media, video gaming and marketing are laggards in MT adoption, mainly because they require high levels of cultural sensitivity and creativity that MT as of today can’t match. That’s not to say MT isn’t making inroads into these areas. For example, world top-10 gaming company Electronic Arts (EA) adopted MT tech quite early in its development. Notably, in areas where content is intended to prompt emotional engagement, EA uses human-edited MT translations. The survey also found that in circumstances that directly impact business revenue, human translation is preferred.
Applying those findings to business activity, MT is useful for quickly transmitting a message to large numbers of people in various locations. Even in this instance, it’s always wise to have a human check the copy. The content lifecycle is also a consideration. For short-lived content, such as product specs on a short run of merchandise, then the return on investment is not there for human translation, which costs more than MT. Here, the requirements for quality and timeliness are key determinants.
Another great use case for MT is where recipients are aware that the content they are consuming is translated by a machine. This enables them to read with caution.
In a sign of just how far MT could go in the future, a group of scientists recently launched a project to decode sperm whale ‘speech’ with a view to enabling whale-human communication. That would be an interesting one to add to the digital translation services already available. The Cetacean Translation Initiative is using AI to understand whales’ clicking sounds, known as codas. The scientists are deploying natural-language processing, which processes spoken and written communication, to that end.
MT is improving all the time, and as it does, it becomes applicable to an ever-expanding set of scenarios. However, we aren’t anywhere near the point where MT is good enough for businesses to abandon Machine Translation Post-Editing (MTPE). Finding the right balance is key, and a professional translation agency will help you navigate the optimal configuration of MT, human translation and MTPE. Get in touch today to speak to an expert and explore your options.