Introduction to the History of AI Translation and Popular Software

Table of Contents

• 1. The Evolution of AI Translation
• 2. Differences Between Conventional Machine Translation Technology and the Latest Machine Translation Technology
• 3. Features of Popular Machine Translation Worldwide
• 4. Summary

1. The Evolution of AI Translation

AI translation (machine translation by computers) first appeared in the 1950s. The technology used initially was rule-based, but statistical machine translation emerged in the late 1980s, followed by neural machine translation in the 2010s.

 

In rule-based machine translation, translation rules are manually created based on dictionaries and grammar. It was necessary to create a vast number of rules, which made development and updates for new terms labor-intensive. The accuracy was not high, and it could mainly translate only formulaic sentences.

 

In statistical machine translation, computers learn the rules instead of humans. They read a large number of pairs of original texts and their translations (such as one million sentences) and learn to statistically associate words and phrases from the original text and the translation based on that data (called a corpus). If additional original texts and translations are collected, it is relatively easy to accommodate new phrases. However, translation between languages with significantly different grammar, such as English and Japanese, is difficult, and the translation accuracy was not yet practical.

 

In addition, there are technologies such as hybrid machine translation that combines statistical machine translation and rule-based methods, as well as example-based machine translation that extracts similar parts from existing pairs of source texts and translations for use in translation. These machine translation methods have improved translation accuracy compared to rule-based and statistical machine translation.

 

Neural machine translation, like statistical machine translation, learns by feeding a large number of pairs of original texts and their translations to a computer. However, by using neural networks and deep learning, which are types of machine learning, it extracts and utilizes far more information for translation than statistical machine learning. Compared to traditional machine translation, the accuracy of translations has significantly improved. The translations are characterized by their fluency, producing natural translations that resemble human translations. With the advent of neural machine translation, machine translation has gained attention and is now widely used in everyday life and business.

2. Differences Between Traditional Machine Translation Technology and Latest Machine Translation Technology

The main difference between traditional automatic translation technologies, such as rule-based machine translation and statistical machine translation, and the latest automatic translation technology, neural machine translation, is the fluency of the translated text. Traditional machine translation often results in unnatural translations that are easily recognizable as machine-generated. Additionally, because translations are done sentence by sentence, the connections between sentences can often be awkward. In contrast, the translations produced by the latest neural machine translation are natural. When translating at the paragraph level, as with DeepL, the connections between sentences in the translated text are natural, making it difficult to distinguish whether the text was translated by a human or by machine translation when reading through the paragraph.

 

On the other hand, there are issues that have become prominent with neural machine translation. These include translation duplication and omissions. In traditional machine translation, since phrases from the original text are translated one by one into the output, duplication and omissions rarely occur. However, in neural machine translation, there can be cases of duplicated translations or omissions. Particularly because the translations produced by neural machine translation are fluent, it can be difficult to identify omissions when reading only the translated text. To find omissions, it is necessary to compare the original text with the translated text.

3. Features of Popular Machine Translation Worldwide

Representative machine translation services include DeepL, Google, and Microsoft, all of which utilize neural machine translation technology. Among these, DeepL is particularly noteworthy. The characteristic of DeepL is its fluency. This fluency is achieved through paragraph translation. By translating text at the paragraph level, it accurately captures context and domain, resulting in not only the appropriate terminology being used compared to sentence-level translation, but also a more natural connection between sentences. As a result, the translated text becomes more fluent.

 

For the latest trends in machine translation and a comparison of DeepL, Google, Microsoft, and Amazon, please see 'Latest Trends in Machine Translation and Comparison of DeepL and Google Translate' (https://www.science.co.jp/nmt/blog/32334/).

4. Summary

AI translation has evolved from rule-based machine translation, statistical machine translation, to neural machine translation. Translations produced by neural machine translation can be more fluent than traditional methods, sometimes indistinguishable from human translations. However, since there can be instances of translation duplication or omissions, it is necessary to compare the original text with the translated text as before.

 

Our company offers the translation software "MTrans for Office" that incorporates machine translation services from DeepL, Google, and Microsoft. It allows for one-click translation of Microsoft Office products (Word, Excel, PowerPoint, Outlook), which helps reduce labor costs. Please check the quality and usability with a 14-day free trial.

 

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