Statistical machine translation has saved businesses a ton of money and time while playing a major role in globalization. In 2022, business owners and their teams must understand the history and capabilities of new technology like machine translation to maintain a competitive edge. Although earlier models weren’t as sophisticated, they formed the foundation for today's neural machine translation systems.
Discouraged by rules-based translation, researchers at IBM proposed one of the first statistical translation models in 1990, which was only 48% accurate when translating English and French sentences. But the machine reduced the work of human translators by 60%. So, the researchers remained hopeful for the future.
Over the years that followed, researchers made significant progress in other language pairs, including German-English, Chinese-English, and Spanish-English. In 2006, Google Translate introduced its own statistical machine translation model, quickly becoming a household name despite its flaws.
So what is statistical machine translation? How does it measure up to other approaches? Let’s take a closer look at SMT, so you can make well-informed decisions when choosing a machine translation model or language service provider to help your business localize.
Statistical machine translation (SMT) is a subfield of [machine translation link to machine translation] that uses mathematical models to translate text from one natural language to another. SMT analyzes prior collections of translations in the language pairing, known as text corpora. This allows the system to determine the probability of an output. Then, it chooses the translation with the highest probability of accuracy.
Since 2014, most industries have moved to neural translation systems. However, many studies show that using a hybrid approach, combining SMT and NMT, increases accuracy. Some language service providers agree and use hybrid systems as an added layer of quality protection.
When first introduced in 1990, SMT was seen as a great improvement compared to the traditional rules-based translation. Researchers refined the early models in an attempt to address the challenges. Their efforts gave rise to several different statistical translation approaches.
The word-based approach is simple, generating one word at a time. However, it has several disadvantages. It does not account for the syntactic structure of the sentence or the context of the word, which can result in disorganized translations that change the meaning of the original text.
The model translates sequences of words. This approach is more complex and overcomes the disadvantages of the word-based approach. By interpreting the syntactic structure of the sentence and context, the translation retains the original text’s meaning. However, phrase-based approaches do not sound as natural.
The model translates syntactic units, improving fluency. Because it can interpret some turns of phrases, these translations are more natural-sounding than the phrase-based approach.
HPBT is a machine translation approach that uses a phrase-based translation model and a hierarchical language model. Using probabilities, this model captures the syntactic and semantic dependencies between words in a sentence, making it the most commonly used model.
The HPBT approach has outperformed conventional phrase-based translation models on a variety of tasks, including machine translation, information retrieval, and question answering. In recent years, the basic concept of HPBT has been extended to other domains such as natural language processing and computer vision.
Statistical machine translation has many advantages over traditional rule-based methods of machine translation.
SMT is much cheaper and faster than rules-based translation or human translation. As such, it saves you money and valuable time, which is critical when competing in the tech, medical, IT, or e-commerce sectors.
Rules-based approaches require rules for each language, and it is difficult to create large dictionaries and compile grammatical rules. So, creating statistical models for multiple languages requires less time and painstaking work than developing separate rule-based systems for each language.
Statistical machine translation can use much larger amounts of data than traditional methods, making it possible to train the models on a very large collection of translated texts. This is especially important for low-resource languages, where such data is the only available resource.
Third, statistical methods can be used to automatically learn the translation rules from data, rather than having to be manually specified by experts. This makes it possible to rapidly adapt the translation system to include new languages or domains without needing expensive human expertise.
SMT systems can generate multiple translations for a given input, which can be useful for applications such as information retrieval, where different users may have different preferences.
Statistical machine translation can generate more fluent and natural-sounding translations than those produced by traditional rule-based methods.
SMT can be slower and more resource-intensive than NMT since it requires more complex algorithms and larger training datasets. The complexity of SMT makes it difficult to understand and debug the system.
On the other hand, NMT is faster and doesn’t require as much training. For example, Google recently revealed its Zero-Shot Translation, which can translate language without any translated training on language pairs.
First, SMT often relies heavily on statistical methods, which can be less accurate than the neural networks used in NMT. Neural networks process languages similar to the way a human brain decodes words, allowing it to be more adaptable to nuances and context.
SMT is more difficult to adapt to new languages and domains since it relies on specific rules or patterns that may not generalize well. As anyone who’s learned a language knows, there are exceptions to every rule. So this strict adherence to rules can result in translations that don’t sound natural.
Finally, because SMT systems rely heavily on probabilities, it is often difficult to determine a confidence estimation for SMT machine translation.
Google Translate began as a statistical machine translation service in 2006. Now, it is a neural machine translation service, including over 133 languages.
The earlier versions of Microsoft Translator are statistical machine translation examples. Like many other machine translation software companies, it now uses neural machine translation. It is part of Azure Cognitive Services.
SYSTRAN was one of the first companies to offer online statistical machine translations. Its commercial machine translation software suite includes a number of tools for translating text.
Many people compare neural translation to statistical translation. While many studies have pointed to the fluency of neural machine translation, others highlight the strengths of statistical translation, concluding that the two should be combined for improved quality.
Some have taken the notion of QA assurance further by combining machine translation systems with machine post-editing services. This hybrid machine translation workflow results in superior quality and accounts for rare word misinterpretations.
Recently, TechBullion interviewed Maya Ronen, the Chief Operating Officer of Tomedes, one of the top language service providers specializing in localization with a certification in Machine Post-Editing. She highlighted the need for hybrid systems featuring skilled human translators.
“If there’s no human involved in the process, it causes problems. If no one checks it or reviews it, that’s where you see problems in translation arise. This is why it’s best to have humans involved before and throughout the process,” she said.
The future of SMT is uncertain as NMT continues to gain popularity and show strong results. Some of its guiding principles may prove useful to NMT. However, SMT still has its advantages and may continue to be used in some domains or for specific language pairs. Ultimately, you’ll have to decide which approach fits your needs depending on each project's specific needs and goals.
One thing is for sure. The future of machine translation is vibrant, showcasing the abilities of AI and human ingenuity at the highest level.
© Copyright 2023 Tomedes All Rights Reserved.