Machine learning and Artificial Intelligence (AI) have both made significant leaps since their launch. More people are now adopting machine learning and AI translation into their daily lives. Essentially, AI translations are the powering technology behind most home assistants, devices, and smart home systems. Google, in 2017, announced the launch of its Neural Machine Translation System (GNMT) and has continued to improve on the technology since then. While significant leaps have been made since then, it is clearly evident that the debate on whether AI translations will replace human translation has come to stay.
A Greek translation services provider notes that these tools have been designed to learn more about each user and provide specialized results; however, they aren’t 100% accurate and reliable. A lot of factors make it nearly impossible to support AI translations and machine learning against human translations. If you wonder why human translations will continue to exist for years to come, below are some reasons to consider.
Machines are Built to Translate Words while Humans Process Ideas
Machine learning has come a long way since its first introduction. However, one basic fact remains unshaken – that machines are built to process their intake. To that extent, it is fair to note that machine translation does not read beyond words. It focuses on the 1s and 0s and provides an output that is based on human programming. On the other hand, human translations come with a bit of sophistication in that humans can sense the idea and context behind each sentence and pass the same across in the right way after translation.
With humans, it is more than converting words from one language to another; it is rather the transference of ideas.
Machines are Incapable of Identifying and Rectifying Translation Errors
Humans can read through, proofread, and edit their translation to ensure premium quality output. This same requirement cannot be expected from machine translation. Machine translation spits out its translation and relies on its human operator to determine the errors and rectify them. In most cases, machine translators offer laughable errors that wipe the context of sentences and give them an entirely new meaning.
Machines are Emotionally Lacking
With machine translation, conveying the deepest sense of regrets or happiness through words may just be another lost nuance. Human communication is laced with nuances that are carried on with specific word choices. These word choices, to machines, are just another word that can be interchanged or translated loosely to mean the closest possible thing in the target language. Machines are also unaware of the context and the cultural background of each word used. Some cultures shy away from certain words that may be loosely used when translating using machines. These are areas where human translators excel.
Machines Lack the Knowledge of Translation Transformations
Linguists adopt several translation transformations during translations that may be hard to get when a machine is used. Humans are vast in the lexis and structure of their sentences and are also aware of language semantics and syntaxes. All of these cannot be expected from machine-generated translations. While these features of each language may appear easy on paper, they stand out when machine outputs are compared with human outputs.
Machines Can’t Tell the Difference Between Homonyms
Homonyms can mean two or more things, depending on how they are used in a sentence. It may be a lot harder to get the best output with machine translations, especially when a document contains a heavy number of homonyms. For example, a document could contain the word “Address” which could easily mean to speak to someone or regard them. It could also mean the location of the person – for example, their home address. Other common homonyms include bark – a dog bark or tree bark, current, electricity reference, water flow, or updated.
Machines Lack Linguistic Lacunae
Linguistic lacunae are words that are found in one language but are lacking in some others. This means that a word in the English language may have no direct translation to another word in, say, Swedish. When dealing with such words like this, machines may completely skip on them, or it can change the entire context of the sentence that is being translated.
Machines Cannot Coin New Terms
While machine learning is improving, it is important to note that machine translations cannot provide new terms and words. Machine translations are focused and restricted on the words that are already known and may have a hard time adopting new terms that would be expected from modern, professional translators. Human translators are more updated on the lingo and would do a better job passing messages across.