How Google Translate uses math to understand languages

In a world where language diversity is enormous, Google Translate stands out as a remarkable tool, bridging the gap between over 134 different languages. This advanced technology, which has evolved significantly since its inception, uses advanced mathematics to transform language into something that computers can understand. This article describes how to do this Google Translate uses mathematical models to understand and translate languages ​​efficiently.

Google translator

The journey of Google Translate began in 2006 with a phrase-based translation system. This initial version worked by pattern matching across large professional translation datasets. When a user enters a sentence for translation, the system breaks it into the longest possible fragments it has seen before and then reassembles it into the target language. However, this approach had its limitations in terms of accuracy and understanding of context.

The real breakthrough came with the introduction of neural networks, or more precisely, transformer models. These models represent a significant step from pattern matching to a more nuanced understanding of language through mathematics.

Turning language into mathematics

The heart of current Google Translate technology is the transformer model. This model revolutionizes the way we process language by converting words into numerical representations, or vectors. Each word in a language is assigned a vector, which is basically a list of numbers. The key insight is that a series of numbers can contain the meaning of a word, allowing the system to perform mathematical operations on these vectors to determine relationships between words.

For example, the relationship “king minus man plus woman equals queen” illustrates how vector arithmetic can capture semantic relationships. Although the specific numbers assigned to words vary from language to language, the relative relationships between them remain consistent, allowing for effective translation.

How does Google Translate work?

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Codec-decoder structure

Google Translate uses a codec architecture to handle translations. The process begins with an encoder that transforms the input text into a context vector, which is a numerical representation of the meaning of the entire sentence. This is achieved through many layers of mathematical operations, most notably matrix multiplication.

Basically, the encoder takes each word, converts it to a vector, and then constructs a large matrix that records the interaction of each word with every other word in the sentence. By matrix multiplication, the system calculates a new set of vectors that represent the meaning of the entire sentence, not just individual words.

Multilingual translation support

The decoder then takes this context vector and performs the reverse operation. Converts the numerical representation back to words in the target language. This step also involves extensive mathematical operations to ensure that the translated sentence is grammatically and contextually correct.

One of the challenges that Google Translate faces is translating between languages ​​that are not directly related, such as Japanese and Zulu. In such cases, the system usually uses English as an intermediary. This process involves translation from Japanese to English and then from English to Zulu. This intermediate step provides greater accuracy because the system is well trained to translate to and from English.

Optical Character Recognition (OCR)

In addition to text translations, Google Translate also supports optical character recognition (OCR) using Google Lens. This technology extracts text from images, making information more accessible, especially when typing is not possible. OCR first identifies text lines and their directions, and then divides the image into pixel fragments, called tokens.

The transformer model encoder processes these tokens to predict the best characters and words. By analyzing context, OCR deals with spelling errors and different text layouts, ensuring accurate extraction even from complex images.

Google Translate’s accuracy relies heavily on extensive training with billions of examples. Engineers continually refine the models through extensive testing with AI evaluators and professional translators. However, it is impossible to test every word combination, and some translations may still lack context or precision.

The system also faces challenges with less formal language, slang, and social media texts due to limited training data. Additionally, translating text on deformable objects such as clothes or packaging can be problematic due to variable angles and poses.

The future of translation

Google is working on adding more features to Google Translate, such as allowing users to improve translations and expanding the range of supported languages. The goal is to eventually support all 6,000-7,000 languages ​​around the world, making information accessible to even more people.

In summary, Google Translate is an example of how advanced mathematics can overcome language barriers. By converting language to numerical data, it facilitates accurate and contextual translations into a wide range of languages, constantly evolving to meet the needs of a diverse audience around the world.