Introduction to Machine Translation Evolution

What if it were possible to eliminate language barriers with the click of a button? We may not be there yet, but advancements in machine translation (MT) are bringing us closer to this reality.

According to a 2023 survey by Nimdzi Insights*, 60% of localization professionals want to increase the use of machine translation in their work. And we’re not talking about the early rule-based systems that struggle with nuance and context. Today, we have sophisticated neural networks that translate fluently and accurately.

In this article, we’ll take a deep dive into the humble beginnings of MT and where it is today.

Early Days: Rule-Based Machine Translation (RBMT)

The origins of machine translation track back to the mid-20th century during the Cold War. There was an urgent need to quickly translate scientific and military documents from Russian to English (and vice versa). As a result, researchers developed the early machine translation systems, now known as rule-based machine translation (RBMT). RBMT operated on a foundation of linguistic rules and dictionaries – here’s how it worked:

1. Analyze: The system would break down the source text into words and phrases.
2. Translate: It would then consult dictionaries and grammar rules to find equivalent words and structures in the target language.
3. Generate: Finally, it would combine the translated words and phrases to create the output text.

RBMT was a groundbreaking concept at the time, but it was far from enough. Languages are full of idioms, double meanings, and cultural references that don’t always translate directly. Early RBMT systems struggled with these nuances, producing clunky, inaccurate, or downright nonsensical translations.

Still, it didn’t seem like a better solution was in sight. A researcher named Yehoshua Bar-Hillel even said that fully automatic high-quality translation might be as likely as a machine that could think.

Eventually, RBMT laid the groundwork for future advancements, such as SYSTRAN in the 1960s. It was used by organizations like the US Air Force and the European Commission. This was the first time a computer could translate 60 sentences of Russian scientific text into English*.

SYSTRAN was still far from perfect, but it was a huge step toward the future of machine translation.

The Statistical Revolution (SMT)

In the 1990s, machine translation took a huge leap forward thanks to a simple idea: using data to teach computers how to translate.

Instead of just relying on handcrafted rules, researchers fed massive amounts of text in multiple languages into their systems. These texts, called parallel corpora, gave the MT systems a ton of real-world examples to learn from. This process, known as statistical machine translation (SMT), analyzes these parallel corpora to identify patterns and probabilities.

For instance, you have a sentence in English and its translation in French. SMT would break down both sentences into smaller phrases and then figure out how often each phrase in one language is paired with a phrase in the other. Then, it’ll crunch through enormous amounts of data and build up a statistical model of how languages work. When it receives a new sentence to translate, SMT uses this model to calculate the most likely translation for each phrase and then come up with the most probable translation.

This data-driven approach is far more effective than RBMT. SMT can handle a wider range of language patterns, produce more fluent translations, and adapt to different writing styles.

A Cambridge study* comparing RBMT and SMT found that the latter consistently outperformed the former in terms of translation quality. This led to SMT becoming the dominant MT choice in the early 2000s. By 2010, Google Translate used SMT* to handle millions of translation requests per day.

But that doesn’t mean SMT was without its challenges. It struggled with complex sentence structures, idiomatic expressions, and subtle nuances of meaning. Occasionally, it would produce grammatically correct translations and lack the natural flow or cultural context of a human translator.

The Neural Network Era (NMT)

Imagine if there was a computer program that could learn from experience, just like the human brain. That’s the basic idea behind artificial neural networks (ANNs) – a machine learning model inspired by how neurons connect and communicate in our brains. Neural machine translation (NMT) network consists of two main parts. The encoder reads the source sentence and converts it into a mathematical representation. Then, the decoder takes the encoded representation and translates it into the target language, word by word.

To make this process even more powerful, NMT uses two smart tricks:

• Attention Mechanisms: These mechanisms act as spotlights, helping the decoder focus on the relevant parts of the source at each step of the translation process.
• Word Embeddings: These represent words as points in a multi-dimensional space. Words with similar meanings are closer together, which allows the model to capture semantic relationships.

As you can see, NMT has completely changed machine translation for the better. Studies have shown that NMT consistently outperforms SMT in translation quality.*

It’s not just in terms of accuracy, either. NMT can produce more natural translations, capture the original text’s tone, and even handle languages with limited resources.

Luckily, NMT gained traction pretty fast, with Baidu launching the first large-scale NMT in 2015. Soon enough, Google released their own version of the system, called Google Neural Machine Translation (GNMT) in 2016. This was followed by the launch of DeepL Translator – even older translation services like Microsoft Translator adopted NMT.

One of the most exciting developments in NMT is the invention of Transformer models. Introduced in 2017, these models are based on a new type of neural network architecture designed for natural language processing tasks.

A Google study* found that their Transformer-based NMT system achieved a 60% reduction in translation errors, which is much higher than that of their previous phrase-based SMT system.

LLMs and Generative AI

Another well-known application of transformer models are large language models (LLMs). Combined with Generative AI technology, these systems can achieve human-like performance in tasks like content generation, summarization or labeling.

It started with the launch of GPT-1, the first Generative Pre-trained Transformer, in 2018.

GPT is a language model pre-trained on large amounts of text data and can perform a wide range of language-related tasks.

GPT-1 was pre-trained only with billions of words and had 117 million parameters. However its successor, launched in 2020, was a game-changer. GTP-3 trained on a corpus of 500 billion words with 175 billion parameters brought us into the world of Large Language Models.

Now, the capabilities of LLMs are way beyond what the inventors of MT engines could have ever predicted.

LLMs can do more than just convert text from one language to another. In recent years, LLMs have become an invaluable tool for all kinds of language tasks. They can capture nuances of meaning, style, and context to a greater degree. This technology is also behind summarization tools that condense lengthy articles into summaries. Question-answering systems and modern multilingual chatbots also use these systems

Generative AI and LLMs have ushered in a new era. These modern systems learn and adapt the more you use them. As these models, like GPT and BARD, get more accessible and popular, generative AI enters its golden age. AI powered LLMs soon may generate human-like text and translations.

As incredible and groundbreaking as it is, these models still have limitations. For instance, data biases can creep into the models, causing inaccurate or unfair results. There are also concerns about using them in sensitive contexts, such as legal or medical settings.

Modern Machine Translation and Its Benefits

Machine translation has come a long way since its origins as a rule-based system. While it initially relied on linguistic rules, it soon began using patterns in data to create more accurate translations. NMT is the most reliable and highest performing machine translation system to date.

The implementation of modern MT is seen in cloud-based AI translation services like Google Translate and DeepL, which are indispensable tools for modern businesses. They provide quality translations and cost nothing compared to traditional translation methods.

Thanks to MT-powered automatic translation tools, companies that want to reach international audiences can now quickly and easily translate their websites into multiple languages. As a result, they can expand their customer base and operate in new markets.

Modern machine translation is changing the game of website translation – here’s how:

• Instant Translations: Modern MT provides good-quality translations in real time even for a huge amount of text.
• Improving Quality: Machine learning algorithms constantly improve MT quality. Models are collecting data and learning from the internet and also can be trained with customized datasets quickly and easily.
• Control Over Terminology: By creating and using glossaries, users can ensure consistent terminology across their translations and avoid repetitive corrections.
• Cost-Effective: Modern MT solutions are typically very affordable.
• Scalability: With 100+ languages available to translate millions of words in minutes, machine translation can scale effortlessly to meet growing needs.
• Efficiency Boost: Automatic machine translation eliminates the need for time-consuming manual processes. It can extract and rapidly translate vast amounts of content making it suitable even for large websites with frequent updates.

Conclusion

Machine translation has come a long way. We’ve seen groundbreaking improvements in translation quality, accessibility, and applications.

The future looks just as promising as the journey. As neural machine translation, large language models, and generative AI continue to evolve, we can expect the translation quality, speed, and accessibility to get even better. This will completely change how we communicate, learn, and do business across borders.

The best part? The benefits of state-of–the-art machine translation are already a reality—this is your chance to leverage them for your website. Explore the possibilities of website translation with Easyling today.