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By Alexandre Johnson-Chalifour
April 9, 2023

Machine Translation: Five Limitations You Need to Know

With chatbot ChatGPT making news by passing a law school exam and almost passing a U.S. medical license exam, it’s now undeniable artificial intelligence (AI) can produce astounding results. Machine translation has also made tremendous progress over the years and is now a must-have disruptive technology that’s already in most translators’ toolboxes. However, for their skills to continue to shine in this era of unprecedented technological progress, language professionals must become familiar with the features of artificial neural networks (or “neural networks”), machine learning and artificial intelligence (AI) systems, especially with their limitations.

Below is an overview of five limitations these technologies have and a few strategies that human translators can use to avoid unpleasant surprises.

Extreme Sensitivity

Neural networks tend to have a different interpretation of things that would appear very similar to the human eye. For example, researchers have shown that when a system using this technology is tasked with identifying the content of an image, you can change the answer you get back only by altering a single pixel in said image. In light of this, you can guess that nearly identical sentences are likely to be translated differently when dealing with language.

Recommendation: Pay close attention to small changes between suggested translations for segments with high match percentages, so your target is consistent.

Human Biases

You might think that AI has no bias, but when it’s data-driven, various biases in its learning data influence it. In 2019, researchers found that one of the algorithms used by the U.S. government to identify patients most likely to need intensive care was racially biased. The AI system used people’s medical expenses to assess their risk level. In doing so, it concluded that Black people were less at risk because they had fewer healthcare expenses. Its conclusion was incorrect since it was based on data showing a systemic problem with access to healthcare for this poorer demographic.

AI systems can also have an availability bias. This is because the machine tends to view what it learns as representative of reality, which is often not the case when the corpus size is small.

Recommendation: Carefully proofread machine-translated sentences to ensure the machine didn’t include unwanted human biases that were inadvertently added during machine learning.

Unclear Reasoning

While computer science remains a mystery to many, AI continues to puzzle even experts. In many cases, it works well, but it’s often hard to understand why it does.

Recommendation: Always check each segment translated by an AI system—no matter how good it is. You never really know how the system got to that result.

Inherent Uncertainty

Since AI systems rely on probability, you have to expect that some of their guesses will be inaccurate. Further, the machine may have high confidence in wildly inaccurate results. This potentially catastrophic limitation is holding back the large-scale deployment of AI in applications where security is critical, such as medicine and self-driving vehicles.

Recommendation: Leverage one of the greatest strengths of human language professionals: their judgment. When there’s room for interpretation, question whether the machine has understood it correctly and then make any necessary changes or add nuance to the target text. If the AI system is configured to learn continuously, it will incorporate your feedback in future work.

Questionable Math Skills

Contrary to what you might think—unlike traditional computing—artificial intelligence is not very good at math! Researchers don’t exactly know why since AI is usually able to solve most problems, provided it’s given enough data and resources to support its learning. Unfortunately, math seems to be the exception.

Recommendation: Pay close attention to anything an AI system has translated related to math, including numerical values and calculations. There’s a high risk of errors!

We hope this overview has been helpful. To learn more about using AI for translation, check out the following articles:

When Should You Use Machine Translation?

Chatbot Training: A New Kind of Language Service

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