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In this article, we draw a parallel between Conventional Translation and Neural Machine Translation, exploring their dimensions of execution, as well as their main differences, highlighting relevant use case scenarios for a better understanding of where they triumph and falter in attaining these goals for industries worldwide
In this technology-endowed 21st century, the topics of Artificial Intelligence and Neural Machine Translation continue to fascinate and to be deployed in many contexts, as more and more industries are ripe for digital transformation and visionary technological advancements.
And with steady global trade and the ever-growing need for international communication between markets and cultures, enterprises are more and more often seeking the integration of streamlined Translation Solutions to lower their costs and sky-rocket their productivity and volume coverage.
Conventional or Human Translation can be defined as the literal word-for-word transformation of a source text into a target language, the first stepping stone on any content’s road towards a new audience, as it provides this new audience with the ability to decipher and understand the source text in their native language.
Human Translation employs linguistic subject matter experts, native speakers of the target language with extensive previous experience in the translation topic they are tackling. Conventional Translation uses Computer-Aided Translation (CAT) tools, Glossaries, and Translation Memories (TMs), which the SMEs harness to translate and culturally align a source and target text, with an inherent focus on correct terminology, grammar, accurate wording, consistency, country-specific language conventions, and style, as well as other project-specific or client and market-specific instructions, guidelines or regulatory requirements.
NMT or Neural Machine Translation differs from Human Translation, being an AI-powered Translation Service, which involves an end-to-end approach for automated translation, taking advantage of deep learning methods and neural networks closely based on the human brain, which allows data to be categorized into various groups and layers. Although loosely based on the principle of Human Translation, the core functionality of NMT consists of pre-existing bilingual databases and automated learning and updating processes.
In comparison to Conventional Translation methods, NMT is certainly not a silver bullet and the wide range of limitations in comparison to standard linguist-based TEP (Translation – Editing – Proofreading) processes is undeniable and subject to continuous reassessment.
|Conventional Translation||Neural Machine Translation|
|Lexical robustness||Limited lexical variety and density|
|Versatile translation format rendition||Limited format rendition|
|Terminological stability||Lexical instability and frequent inaccuracies|
|End-to-end translation workflow streamlining||Need for post-editing by at least 1 linguist|
It has been found that although NMT is the most developed form of machine translation in terms of contextual and lexical grasp, it still deploys considerably limited lexical variety and density in comparison to human-driven translation.
This “lack of lexical robustness” results in the limited applicability of NMT in many domains or high-profile business sectors requiring specific terminology (for examples exact texts, medical texts, heavily endowed with specific terminology, or texts rendered in Asian language combinations which employ a different ideographic-based writing system), which cannot tolerate instability in terms of lexical inaccuracy and a lack of versatility.
A favourable advantage of Conventional Human Translation, in comparison to NMT, is the rendition of translations in a wide range of format types. Specialized Language Service Providers employ state-of-the-art translation security systems, skilled professionals and up-to-date technologies in order to properly manage an end-to-end Translation workflow.
Furthermore, with every neural network comes great responsibility, that more often than not, NMT cannot fulfil, as Neural Machine Translation synapses cannot perform efficiently without the expertise and decision-making power of the human engineers and architects, requiring post-editing by at least one linguist and significant training with large quantities of linguistic data.
In this article we looked at the concept of Conventional or Human Translation in comparison to NMT or Neural Machine Translation from a multitude of standpoints, observing key features, applications, and core differentiators in order to gain a better understanding of how these two popular Translation Services come into play, the main overarching observation being the importance of ensuring quality and consistency throughout the end-to-end translation workflow.
At AD VERBUM, we take great pride in our streamlined services portfolio, bringing the best of both the Human and AI-driven Translation worlds, as we are constantly striving to strengthen the translation digital synapses and guide you on your Translation and Localization journey through in-depth knowledge and expertise in a wide range of fields, harnessing state-of-the-art translation technology and processes.
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