How Google Translate, once criticized for translation errors, became a revolutionary tool with deep learning-based GNMT

Google Translate, which was criticized for its translation errors a few years ago, has greatly improved the naturalness and accuracy of its translations with the introduction of deep learning-based GNMT technology, which, along with advances in artificial intelligence, is expected to make translations even more perfect in the future.

 

Most people who have used Google Translate have experienced errors in Google Translate at some point in their lives. Before using Google Translate, you may have had the expectation that Google Translate would do everything for you, even if you didn’t know the language, but after using Google Translate, that expectation has gone out the window. Google Translate has recently undergone a transformation that has made it a much better experience than it was a few years ago. The context of the translation has become much more natural and even poetic expressions are now interpretable.
Google Translate has always struggled to handle subtle nuances, especially between languages. This has led to many users having to go back to Google Translate to correct or reinterpret its output. In recent years, however, this hassle has been greatly reduced. Thanks to the introduction of new technologies, Google Translate is able to provide much more sophisticated and accurate translations than before, which is a huge step forward for global communication.
The reason for this meteoric rise is the introduction of the Neural Machine Translation System (GNMT) to the Google Translate service. Before the introduction of GNMT, the system behind Google Translate was a phrase-based machine translation system. This system was based on a database of grammatical rules and semantics of the language inputted by humans, and it had to translate each word or phrase in a given sentence one by one and then assemble them into a sentence like a puzzle. Because of this, the sentence structure itself had a strong sense of being played with separately, and the word order and context of the translated sentence itself were very unnatural to read. Of course, the author’s meaning and intention in the sentence could not be grasped. Since Google has been providing translation services based on this system, many users have experienced embarrassing translation results.
However, the newly introduced GNMT technology is a translation technology that utilizes deep learning, a core technology of artificial intelligence. The GNMT technology recognizes the flow of the entire sentence and understands the author’s purpose in the sentence to provide a translation, making the interpretation noticeably smoother. While the introduction of GNMT is a major innovation in itself, it’s important to understand the basic concepts of deep learning in order to understand the changes it has brought about.
So, what is deep learning, the core technology of artificial intelligence that has brought us to this point? Deep learning is a general-purpose artificial intelligence algorithm that was used in AlphaGo, the computer Go program that competed against Lee Sedol. It’s a word you’ve probably heard before, as it’s often in the public eye. Along with this, you may have heard that AlphaGo’s power consumption was enormous during the great battle between Lee Sedol and AlphaGo. It was said to have used 1,200 CPUs, which is roughly the equivalent of 300 computers. These enormous computers study like humans before playing against Lee Sedol. They study what trends they can win and what trends they can lose. AlphaGo learns patterns by analyzing existing moves and creating new moves from them, and when it has learned enough, it plays against Lee Sedol. During the game, AlphaGo looks for the pattern that most closely resembles the current move among the moves it has studied. AlphaGo learns by orienting itself towards the winning move. Just like a human learns. Programs trained in this way are able to perform millions or tens of millions of calculations faster than humans, sometimes outperforming them.
As you can see, deep learning is a form of artificial intelligence that evolved from artificial neural networks, which utilize an input-output layer of information similar to neurons in the brain to learn from data. An artificial neural network, which predates deep learning, is an algorithm that is modeled after the human brain to process information in a similar way to the human brain. The human brain is made up of structural units called neurons, and experience has taught us that it has a number of specific functions, such as pattern recognition and cognition. These neural networks are made possible by the increasing power of computers. However, there are some problems with this technology. They have a slow learning time and lose their direction of change as the number of layers increases. Deep learning has overcome many of these shortcomings.
The introduction of deep learning hasn’t just improved the performance of Google Translate, but has opened up a wide range of applications. For example, in the medical field, diagnostic systems utilizing deep learning can play an important role in detecting certain diseases early and suggesting treatment methods. As you can see, deep learning is increasingly permeating many aspects of our lives, and the possibilities are endless.
GNMT technology, which applies the above deep learning techniques, does not match words or sentences 1:1 like humans do. GNMT technology recognizes the entire sentence as a unit of translation, and is able to grasp the context and reflect it in the results. GNMTs also analyze and learn from existing translations, and in the process, GNMTs can improve their performance by modifying the connections between artificial neural networks.
To evaluate the performance of GNMT, Google’s researchers selected sentences from Wikipedia’s commentaries and news articles and translated them into several languages. They then put them side-by-side with translations from Google’s existing system and human translators, and asked human evaluators to rate the quality of the translations. The results showed that Chinese to English translation, a notoriously tricky language, scored significantly better than our existing system. Translations between some languages also scored close to human translation in terms of accuracy. However, translations between Indian and European languages lagged behind. “It should be noted that the sentences selected were well-crafted short sentences,” the authors emphasize.
Google Translate is powered by deep learning, a key technology in artificial intelligence. Google Translate has been able to accumulate big data by data mining the internet and translate entire sentences as a unit. Since the translation unit has been expanded from words and phrases to sentences, the translation results have made a noticeable leap compared to the past. Barak Turovsky, Head of Product Management, Google Translate, commented on this development “Neural machine translation technology reduces the likelihood of errors by up to 85%. This is a big evolution than what we have achieved in the last 10 years.”
But the reality is that there is a lot of variation when it comes to translating languages. Even within a language, there are individual differences and dialects, and languages change over time. Furthermore, when it comes to translating playful or poetic expressions, the interpretation will be even more imprecise and clumsy. Some have argued that neural network translation technology is less accurate than phrase-based machine translation systems. However, deep learning is at the core of the technology behind Google Translate, so the machine’s brain is accumulating translation data as you read this. Over time, the machine will also be able to self-learn and collect information about the data it has just accumulated. As this accumulates, it will eventually be able to translate in ways that are currently lacking. In fact, Google has admitted that its translations are not yet as good as human translations and that there are many errors. However, the company emphasized that it will evolve to near perfection as it accumulates learning experience and advances in deep learning-based AI and related technologies. We can only hope that Google Translate, with its neural network translation technology, will be able to break down language barriers in the near future.