Machine translation (MT) is an exciting technology based on artificial intelligence (AI), which can automatically translate text or speech from one language to another. This interest continues to grow rapidly as more and more advanced and widely available MT services come from companies like Google and Amazon.
Nevertheless, some persistent myths and misconceptions about machine translation produce needless fear, uncertainty, and doubt in those who might use MT. This article will debunk 5 common machine translation myths with facts and insights both laypeople and experts should know.
Myth #1: Machine Translation Produces Gibberish
In recent years, advances in deep learning have made machine translation almost unrecognizably better. Besides its machine translating applications, the latest versions of AI-powered MT systems provide high-quality translation across commonly used language pairs such as English/Spanish and English/French. Translations now don’t sound like complete gibberish anymore.
However, MT still presents challenges for lower-resourced languages such as Icelandic or Malay. Niche domains like legal or medical content that are full of technical terms do. Despite the above, modern neural machine translation (NMT) models already produce translations that can be gisted to inform the size resource bottleneck. Quality continues improving every year.
Nevertheless, official documents, for example, should never be translated entirely through MT, as a mistake can be very costly. Read more about translating official documents: Visit RapidTranslate
Myth #2: Machine Translation Will Never Match Human Translation
Specialized human translators will not be replaced by generic machine translation any time soon. In the last few years, however, machine translation has reached parity with average bilingual humans in constrained domains like IT in quality evaluations by Microsoft and Facebook.
Once MT reaches professional levels with regards to the quantity of domain it can translate, there is really little more for translators to work on than stylistic refinement and conveying subtle nuances vs. translating bulk content word for word. Linguistic skills will still be needed but specialized. The routine work will be done by AI.
Myth #3: Machine Translation Threatens Translator Jobs
The translation industry currently faces a talent shortage, not a lack of work. The spread of multilingual content through globalization means demand grows faster than qualified translators enter the field each year. MT streamlines basic translation, allowing human translators to keep up with surging content volumes.
Skilled translators also must edit MT, which saves substantial time compared to translating from scratch. By increasing productivity, machine translation boosts translators’ earning potential as they focus on tasks only humans can do well. MT grows the overall translation market rather than reduces jobs.
Myth #4: Post-Editing Machine Translation is Too Expensive
Editing machine-generated translations rather than translating from scratch boosts translator productivity on average between 1.5 and 2X for domains with high MT quality. This saves costs and project timelines up to 43% or more. When the budget is limited, post-editing machine translation stretches dollars further than human translation alone.
However, the productivity gain from post-editing varies by language pair, content type, MT quality, and translator skill. Realistic expectations are key. Productivity metrics should be tracked separately for human translation and post-editing tasks. When optimized, post-editing speeds end-to-end translation.
Myth #5: Machine Translation Quality Plateaus Quickly
Reality: Early statistical machine translation (SMT) systems reached quality plateaus after a few years of data training. However, neural machine translation (NMT) models driven by deep learning continue yielding exponential quality improvements with more data and computing power.
As models grow ever larger and more data feeds them daily, machine translation keeps getting noticeably better. Progress will eventually plateau, but likely not for a while. Expect steady quality improvements for years.
ML Translation or Human Translation: Still a Big Gap in Quality?
While machine translation quality improves every year, there remains a significant gap compared to specialized human translators in many cases. Human cognition still exceeds AI in areas like:
- Understanding implied context and double meanings
- Translating cultural references and humor
- Maintaining the author’s original tone and style
- Catching errors in the source content
- Ensuring terminology consistency
- Communicating complex, nuanced ideas
Human translators also more skillfully compose flowing, grammatically polished translations tailored for the target audience and context. This helps content resonate better across markets.
However, not every translation job requires the finesse of bilingual subject matter experts. For simple content that just needs the gist, like internal communications or basic instructions, machine translation often suffices in commonly translated languages. However, the quality gap for humans may persist for higher-stakes content.
Customizing Machine Translation for Your Content
Out-of-the-box machine translation models work decently for general content. However, training the AI on your company’s specific documents can dramatically improve quality.
Adapting models to your domain teaches the system your terminology, writing style, and context. This allows much more accurate translations tailored to your business needs rather than a one-size-fits-all approach.
Customizing requires large volumes of existing human translations to train machine translation models on. So, it only works if your company already has substantial translated content. While this investment is prohibitive for smaller companies, large enterprises often customize machine translation for efficiency gains that outweigh the costs.
With a customized system, machine translation quality may even approach specialized human translators for certain content types. This makes machine translation more viable for your highest-priority documents. Consider your content and quality requirements when weighing customization benefits.
Key Takeaways: Machine Translation Reality vs. Myth
- While no longer producing primarily gibberish, machine translation still cannot match highly skilled human translators for publishing high-value content requiring finesse, nuance, and style. The output gap persists for complex translations.
- However, quality is already sufficient to understand the essence of more straightforward content that does not require linguistic perfection, like internal communications. Machine translation works fine for basic gisting purposes.
- Customizing out-of-the-box systems by training them on your company’s existing human-translated documents can dramatically improve quality by teaching terminology, style, and domain relevance.
- Machine translation will likely not replace specialized human translators entirely anytime soon but rather act as a productivity multiplier, allowing more content to be translated at acceptable quality levels.
- For most use cases beyond formally published documents, carefully evaluated machine translation boosts reach and access with “good enough” quality that keeps improving with technological progress.
Should You Use Machine Translation?
Let’s hope that a move to explore machine translation myths with their reality might help to set realistic expectations of what AI translation can and cannot do now. The point that should be most obvious is that modern machine translation is imperfect compared to human translators but good enough to understand the core of simple content in relevant languages and domains.
Machine translation may not yet be sufficient for publishing novels and poetry. However, it generates sufficient draft translations of information for many companies to conduct business with. Technology also empowers the average consumer to communicate globally. After dispelling misconceptions, the benefits machine translation offers likely outweigh any remaining flaws for many applications.