Machine translation, artificial intelligence and process automation are changing the game when it comes to translation services. No longer is human intervention required to translate documents or text from one language to another. Instead, machine translation can handle this task quickly and efficiently, with results that are often just as accurate as those produced by humans.
To support language service providers like The Hello Co., there's a range of Translation Management Systems (TMS) in the market that employs some form of machine translation capability - knowing which one is in use by your language services vendor is your first step in understanding the level of translation quality you can expect from your LSP.
There are two categories here that buyers need to be informed about.
Statistical machine translation (SMT) is done by analysing existing translations and defining rules most suited to translating a particular sentence. Feeding the SMT more data in the required languages will give it a higher statistical probability of outputting a more accurate translation. However, whilst they can offer literal translations of content, some challenges still need to be addressed before they can replace human translators completely. For example, these machine translations can sometimes produce inaccurate results, mainly when translating idiomatic expressions or colloquialisms. Additionally, SMTs do not have the programmatic capability to understand nuances associated with the intent of content messaging and nuances related to localisation according to region.
Neural machine translation (NMT), on the other hand, is processed through a neural network. Each neuron in the network is a mathematical function that processes data. The initial calibration or “training” is done by feeding examples into the neural network and making adjustments based on how much error in the output there was. This means that as the network is continually used, it will continue to fine-tune itself to provide better results. Due to the self-learning models powering NMTs, they can often be a much more reliable solution than SMT and other legacy forms of MT. They can also better consider context and, as a result, provide results that have a more human-like feel to them. This means the NMTs get smarter with more use over time as it stores previous translations into memories for future use and as a baseline for learning - this also means your future costs start to reduce.
First, you need to work with a partner that employs the latest TMS capabilities to deliver speed, cost containment and quality. But what capabilities underpin speed, cost containment and quality?
Speed: NMTs must be embedded into any TMS technology your LSP vendor uses.
Cost Containment: Native translation memories and glossaries must reside in the TMS architecture. Not only will this provide a level of quality standardisation, but it will also assist the NMT in understanding your tone of voice. In addition, this will reduce costs as NMTs will reuse previous translations where appropriate, meaning you only pay for newly translated content.
Quality: Real-time collaboration with professional linguists who can correct the output of NMTs where needed pre-production which ensures that the NMTs learn from the experts for the future and your content has been independently vetted before production-ready files are provided.
The Hello Co. works with a highly sophisticated TMS Technology platform with extensive automated quality assurance features within the core application architecture. This produces superior translation results for our clients. These features include:
To learn more about our Translations Solutions, say hello today.