Machine translation, in recent times, has become a buzzword in the translation industry for a lot of good reasons. Its technology has enabled translation companies to offer faster turnaround times for projects while at the same time ensuring substantial cost savings.
But in-between the media hype and sensationalism lies a segment to MT often not talked about. There are significant issues with machine translation that inhibits adoption in its entirety. Hence, MT’s best use-cases often involve supplementing it with human input.
And it is not rocket science to know that by identifying these issues, LSP’s can find better ways to navigate them to get the best out of machine translation.
In this post, we would be going deeper into some of these challenges associated with machine translation.
What are the issues with machine translation?
Although MT has come a long way and witnessed recent improvements, especially with the advent of Google’s neural machine translation (NMT) system, it has not really done much to solve some of the underlying issues.
Perhaps the main concern with machine translation lies in the inaccuracies of the translated text, particularly when dealing with contextual meanings or translation of words that are technical or culturally sensitive.
At the risk of sounding critical, there are still many ways where machine translation stalls, but we would be looking at them from these three main areas;
- Machine translations are still prone to errors
MT has gravitated away from the word-to-word system of translation with the emergence of neural networking models.
And yes, this new system may have proved to be useful in decoding some context of the most relevant translation (an inherent problem of word-based translation), but it still leads to errors when translating sentences with idiomatic expressions, humor, or satire.
These errors can either be in the form of missing or incorrect words, word order at both phrase or word levels.
Also, attempting to translate certain terminologies oftentimes could lead to inconsistent or mistranslated output, and the same could be said for uncommon (technical) terms.
- Poor quality output
Building on our first point, it is only natural that inherent errors during translation result in a below-par quality of the final output. And this goes against the assertions of big tech companies (Microsoft), who claim that their MT engine has attained human-level parity.
(There are a lot of controversies surrounding this)
But this claim is easily put to rest when the translated output demands it be of a high standard, which is the requirement of most translation projects in the real world.
Holding machine translation to a high standard quickly reveals that its quality usually gravitates to the inferior side, especially when translating unrelated language pairs.
- Transcreation fail
Machine translation’s lack of cognitive thinking makes performing creative translation impossible.
From knowing when to omit or translate puns to even going as far as creating incorporating touches of humor into translations, which makes the final output feel original. All these little decisions that a qualified human translator makes on a whim while translating are usually inconceivable for even the most sophisticated machine translation engines.
And the same extends to localization. If the demands of a project require tailoring the content to suit a particular local culture, where the content does not stay the same but only the meaning, only human hands can achieve such feats. As no amount of training data would be able to perform the task to satisfactory levels.
The way forward
Our above assertions are not in any way meant to trivialize machine translation. Make no mistake, machine translation has transformed workflows and improved the efficiency of many LSP’s and advances in AI and machine learning can only lead to more improvements in its applicability.
cMost importantly, we should note that machine translation’s best performance for the foreseeable future would always be paired to post-editing (MTPE) with an experienced human translator. But it should also be said that the quality from MT must be good enough otherwise it defeats the purpose of utilizing it in the first place.
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