Are Human Translators Still Viable in 2023?

In previous blog articles, we have discussed the detriments of over-reliance on statistical machine translations, such as Google Translate, exploring how their phrase recognition systems often lead to mistranslations and altered messages. However, continued advancements in machine translation since our 2018 blog article, including neural machine translation, warrant another look into the current field of machine translations and the continued viability of human translators.

Differences between Neural and Statistical Machine Translations; Continued Advancements

With statistical machine translations, data is drawn from both the source language and the target language and is used to guess or predict what translation would be appropriate to use. This guess can be word, phrase, or syntax based, but the element of uncertainty is still present. While the machine will make its best judgement, it could very well be the wrong one in a certain context of the translation.

With neural machine translations (NMT), data is gathered and processed through a system that resembles the human brain’s neural network, consisting of several layers that a translation must go through before the final translation. NMT also exhibits deep learning, meaning it will teach itself linguistic rules and patterns through usage, creating a translation memory to better predict the translation.  Businesses conducting research into this field’s development were Facebook, Amazon, and Google, all three with a global presence that must be addressed on an efficient scale.

Since 2018, even Microsoft Word has integrated a translator, supporting languages such as Spanish. Translation results are as instantaneous as highlighting text and clicking one button. With further translation integration reaching even the most basic and often archaic form of word processor, are human translators still viable in a 2023 market?

Human Translators’ Continued Viability

Human translators still hold a valuable place in the translation industry through their assurance of quality and assurance of knowledge. While both statistical and neural machine translations provide instantaneous results, many companies still implement a human translator for post-editing work, knowing that quality may still lack that a human element can imbue into a project. Furthermore, both statistical and neural machine translations make predictions, guesses as to what would be the best translation based on the context, while human translators know the best contextual translation. They can analyze not only singular sentences or phrases, but take into account the entire passage, ensuring coherency across the entire document. This knowledge guarantees a polished product, one that businesses can easily distribute internally or externally with the assurance of quality and accuracy. In a world that is increasingly on-demand, it still pays for businesses to insure they reach their audiences with clear, defined messages that are received favorably the first time they are delivered. This insurance is only guaranteed by the human translator and editor.