We often present clients with guidance on how to work with interpreters, and frequently get asked about AI. This is because many people are waiting for the day that they can simply go online and use AI to seamlessly translate between two different languages, but we would like to say it out loud here: THAT DAY HAS YET TO COME.

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Please also check out our playlist for Chinese localization case studies: https://www.youtube.com/playlist?list=PLO-QGEbwcTr14xqfiR38Mp-EhHAmclsUY

We localized the Interpreters and Music video into traditional Chinese as an example to compare translation accuracy between humans versus AI and to identify some classic AI issues.

One of the biggest weaknesses of AI is that it often struggles with names. For instance, the name “Laura” was translated into both “蘿拉” and “勞拉.” When we saw this inconsistency in names, we looked at each other with amusement because this happens all the time. Some may say AI spelling names incorrectly isn’t a big deal since it’s an easy fix. However, for those people, we’d like to share a real-life example.

In a lease contract we worked on, Paragraph 1 said that the landlord shall be known as ‘A’ and the tenant as ‘B’. Paragraph 2 called the landlord ‘C’ and the tenant ‘D’. This was a document with 30,000 words that a client asked us to quote for reviewing the translation, which had probably been done by an AI. Just in terms of reviewing names, how much effort would it take to find out if there were places that call the landlord “E” and the tenant “F” and so on? Not to mention all the work it would take to find other mistakes that humans typically need several rounds of review to detect.

Our analysis also uncovered that AI defaults to using the pronoun “你,” referring to males and offering no female form “妳.”

AI have translated love song titles like “Suddenly Missing You” and “Stuck on You” into traditional Chinese, using the male form. The male singers may not prefer using the male form of “you” in their love song titles. Otherwise, a native speaker in traditional Chinese would feel kind of strange, reading it.

We inserted line breaks on messages that appear in the video. With line breaks, AI seemed to lose the context of the lines.

Line breaks are important. We are often requested to insert line breaks in Asian language marketing materials. Take Japanese line breaks as an example. There are some basic rules for line breaks or how to break words up, but at the same time, there are a lot of exceptions, which humans can easily catch if they understand Japanese, but not AI. In other words, humans break things apart (debriefing) and put them together in a creative way, which AI is just not capable of.

It turns out that AI struggles to translate any segment accurately and, at times, produces unnatural and contextually absurd translations. As shown in the screenshot below, even with a relatively short source text, the quality of AI translation was unbelievably subpar.

AI translated “interpretation” as “explanation” due to a lack of context.
AI translated “Performance” to machine’s performance rather than that of the interpreter’s.
AI mistakenly translated the meaning of “like” as “to be fond of” instead of “similar to.”
AI word-for-word translation for “big heart” doesn’t make sense to a Chinese audience.

It’s clear to us that AI is not able to handle messages that are broken down by line breaks. This then leads us to a question: How well could AI handle entire messages without line breaks?

We conducted a retest by removing all the line breaks on messages. In this attempt, the text was formatted in a more machine-friendly way to enhance AI’s understanding. But even so, post-editing remained an essential step, with 80% of the segments requiring significant human intervention. Without this crucial step, AI translations either come across as rigid and less relatable to our audience, or contain mistranslations. Below are some examples.

Example 1:
The AI translation appears rather stiff because the word “sync” was translated literally. The audience might wonder what it means to “sync” one language to another. Human translators are able to further explain the context of sync, that is, interpreters “listen to one language and convey it in another language.”

Example 2:
AI translated “more emotionally acute” as “more impatient,” which not only deviates from the intended meaning of the source, but also negates the impact of the word “music”. During post-editing, we replaced it with “more emotionally sensitive,” which is more contextually accurate.

Example 3:
AI did word-for-word translation again. It doesn’t sound like what a normal person would say in Chinese. As a dynamic language, Chinese favors verbs over nouns and usually keeps sentences short. Therefore, in post-editing, we restructured the sentence to make it fit a typical Chinese writing style, and flow more naturally.

Example 4:
AI’s translation of “concentration” lacked clarity. Without referring to the source, it was hard to grasp the intended meaning. So, we opted for a more precise choice of words.

Example 5:
AI does a literal translation, full of ambiguity and rigidity, which doesn’t make clear sense to a Chinese audience.

Translation is supposed to flow naturally to engage the audience. It is the more immersive and relatable experiences that make humans feel comfortable. These are exactly the areas where we as interpreters and translators can contribute to.

There may be a lot of gloom and doom from some in the community who think that their jobs are at risk, however, the reality is that we’re training AI to speak our language, but they aren’t able to fully understand it like we can. They can process it, try and find the corresponding pattern in their database, and come to a conclusion that they think is right, but they won’t always be. That’s where interpreters and translators will always have the edge over AI. Human creativity and our ability to understand what’s important, and the culture embedded in it, enables us to make sure that we are conveying the intended message.

We tried one of the latest AI platforms to translate one of our office videos into Mandarin.

While we were impressed by the seamless process and the voice cloning feature that enhanced voice modulation, we couldn’t help but notice pronunciation and translation errors in the generated video. Given that Mandarin Chinese is a tonal language, tones can become a source of misunderstanding if not pronounced correctly. The chosen video introduces the rental service of our conference room, making “conference” a high-frequency word. However, throughout the video, AI consistently pronounces the Chinese word for “conference,” as “memory,” with tones differing from the former. Also, “state-of-the-art” in Chinese is pronounced the same way that “cash” is. This could undoubtedly complicate the message we aim to convey if left alone.

The translation issues we caught are mostly recurring problems caused by machine translation as discussed above. Take the first sentence as an example. AI translated “Looking for a conference room to have a meeting over video or in person?” as “Can you look for a conference room via video or in person meeting?” AI’s rendition deviates from the original meaning, which is likely caused by line breaks, leading to confusion and miscommunication. Such discrepancies underscore the importance of post-editing and human intervention to refine machine-generated translations.

Our conclusion becomes clear.

In this age of AI becoming more prevalent, humans just need to work smarter to beat out AI. As individuals in an evolving world, it’s important to accept technological advancements, but also understand that AI lacks creativity, individuality, improvisation capability, and the understanding of human cultures. That’s how humans can break through and go beyond AI’s limitations.

Ancient Egyptians, Greeks, and Romans

Evidence of interpreters can be seen as far back as 300 BC with the depictions of the Princes of Elephantine. The two were apparently bilingual and were integral to military operations and trade between Egypt, Nubia, and Sudan.

Alexander the Great from Ancient Greece also employed interpreters for communication with India. When Romans dominated society, it was common for them to be fluent not only in Latin, but also Greek from tutors and slaves so as to not have to rely too heavily on interpreters. Roman translations of Greek works are part of what gives us so much mythos to pull from as well.

Of course, interpretation and translation from other languages was a regular event for dealing with outside cultures. For example, in 202 BC, Scipio and Hannibal met together in the presence of interpreters, in what we might call today as a conference interpretation event.

The Bible

In 382 AD, Pope Damasus had St. Jerome translate biblical texts into what is now known as the Vulgate Bible. This was for a form of Latin that the church would use. The history of biblical translations is rich and complex, but noteworthy is Martin Luther’s German translation in the 1500s that allowed the common people to read and understand the word of God for themselves.

Columbus and Cortes

When Christopher Columbus headed to what he thought was India, he hired the interpreter Luis De Torres, who was fluent in Chaldean, Arabic, and Hebrew. However, when he landed in the Caribbean, he decided to capture native Taino people to teach them Spanish and spread Christianity.

Similarly, when Hernan Cortes began his conquest of Mexico, he acquired about twenty enslaved women, one of whom was “La Malinche” as she is known today. He chose her as his consort, and she also served as an interpreter from Nahautl into Mayan, which was interpreted into Spanish by another member of Cortes’s crew.

John Dryden’s Translation Theory

The Ancient Greeks discussed translation theory first and then in 1680, John Dryden proposed three major ideas for the modern era of translation. First was “metaphrase,” which refers to a literal translation; second was “paraphrase,” which refers to translating the meaning more than the exact wording; and lastly was “imitation,” which refers to creative translation of the text.

While these wordings are not often used today, the ideas certainly are. You might hear people talking about “literal translation,” “translation of meaning,” or “transcreation” instead.

“Machine-assisted human translation vs. human-assisted machine translation” was the topic that won the Monterey Language Services Tech Contest, which took place on Monterey Language Services’ Facebook page and ended today at noon.

Participants were mainly from the Chinese program of the Translation and Interpretation School of the Monterey Institute of International Studies, which is the largest program in the school. Congratulations to Qianxi, the winner, and all who participated! (See his victory speech here.) We felt your energy and it was breathtaking. We hope to see you next time.

The 36 comments touch upon the future for translators, development of language technology and machine translation, CAT tools such as Trados, MemoQ, Wordfast, power of machine translation, human translators versus machine translators, translation software, how machines can improve efficiency and productivity for translators, a real life story with Google Translate, training machines to translate, coding and decoding, translation segmentation, machine language, repetitions in text, subject knowledge and specialization, theory versus reality, and more.

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