Machine translation has come a long way since its inception in the 1950s. From the old rule-based programming to sophisticated artificial intelligence, the evolution of machine translation is a fascinating phenomenon! Translation is a constantly evolving field. As technology advances, more and more sophisticated automated translation tools are created. This article discusses the various machine translation terms often used interchangeably, the influence of Machine and Deep Learning on translation quality, and what the future holds for professional translation. Get ready for an exciting journey into the world of computer-assisted translation!
If you're unfamiliar with some of the terms, don’t worry! We’ve included a glossary at the end with simple definitions.
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The Evolution of Machine Translation
Several terms are used to describe technology-based translation tools, such as machine translation, automatic or automated translation, neural machine translation (NMT) and artificial intelligence (AI). While these terms are often used interchangeably, they have specific meanings and applications. Let’s explore the differences between these terms and their implications in translation.
Machine translation is old technology. It uses predefined rules to translate texts from one language to another. These rules are often based on linguistic models identifying grammatical structures and vocabulary correspondences between two languages. Machine translation systems are often programmed to translate sentences using syntax patterns without really understanding the text’s meaning. Therefore, machine translation is limited to simple translations.
Automatic (or automated) translation is a subcategory of machine translation that uses artificial intelligence (AI) and Machine Learning to analyze and understand the source and target languages for translation.
Machine translation and automatic translation are two terms often used interchangeably. However, they have slightly different meanings. Machine translation is a more general term that describes any form of translation performed by a machine, whether it is software or a computer application. Machine translation includes automatic translation technologies, Computer-Assisted Translation (CAT) systems, web-based dictionaries or online translation tools that don't rely on artificial intelligence.
On the other hand, Neural Machine Translation (NMT) uses Deep Learning algorithms to translate texts. This technology uses artificial neural networks to learn language word correspondences and grammatical structures. NMT systems can understand the meaning of the text for more accurate and consistent translations. It can also be adapted to specialized subject fields for translations in specific contexts. However, like any machine translation, the technology isn’t error-proof.
Artificial intelligence (AI) is a technology that uses Machine Learning algorithms to “learn” to translate texts autonomously. Large amounts of multilingual data power AI systems that understand the meaning of the text for more accurate and consistent translations. Like NMT, AI systems can also be adapted to specialized fields, such as law or medicine, for translations in specific contexts.
The above image is for illustrative purposes only. As you can see, there is a much more significant gap in progress between 1950 and 1980, while the last decades show more consistent improvement. If we were to plot a progress curve, it would show that progress was slow at the beginning for roughly 30 years and eventually spiked at an exponential rate. This raises the question of what is yet to come in the near future.
The Difference Between Machine and Deep Learning Algorithms
Machine Learning and Deep Learning are data analysis methods that use algorithms to learn from data. Although both types of algorithms are part of AI, in the context of translation, they are both machine translation models that use Machine Learning methods to learn to translate sentences.
Machine Learning algorithms, or statistical machine translation, analyze bilingual text corpora to learn how to translate sentences. They rely on statistics to make translation decisions. These models can produce decent-quality translations, but their accuracy depends on the quantity and quality of the training data used.
Deep Learning algorithms, on the other hand, are an advanced form of Machine Learning that uses artificial neural networks to learn to translate sentences. Deep Learning models can understand abstract features of sentences and create semantic representations for words. This technique allows Deep Learning models to achieve better translation results than traditional Machine Learning models, even with less training data.
Overall, the main difference between Machine and Deep Learning algorithms in the context of translation is the technique used to learn to translate sentences. Deep Learning models are more sophisticated than traditional Machine Learning models but require more computing power and training data to be effective.
AI’s Involvement in Translation Since the Rise of NMT
The rise of NMT introduced in 2014 is a major development in Computer-Assisted Translation (CAT). One of the key features of NMT is that it is based on Machine Learning, so the more it’s used, the better it gets.
The involvement of AI in translation ensures that Deep Learning algorithms, such as those used in NMT, can significantly improve the quality of machine translation. Deep Learning algorithms allow NMT systems to understand the context and structure of the source language and the nuances of the target language, resulting in a more accurate and natural translation.
AI can also help professional translators by providing more sophisticated CAT tools. For example, to automatically find grammar and syntax errors, and suggest corrections. Translators can also use tools to speed up their work by automating specific repetitive tasks, such as translating common, repetitive sentences or reviewing technical terminology.
During my university years from 2016 to 2020, I got to work with NMT tools like DeepL, which was somewhat helpful but fell short of expectations. To be fair, DeepL had just launched in 2017, so it was still in development. However, in just five years, NMT saw a dramatic improvement in performance and results, far exceeding what could be imagined not so long ago.
This rapid evolution is a testament to NMT’s impressive potential. It suggests that even greater improvements can be expected in the near future. After all, NMT is constantly evolving, taking advantage of technological advances to improve steadily.
Moreover, continued improvements in voice recognition, natural language processing and language understanding have paved the way for rapid advances in machine translation. So, it’s exciting to think about the possibilities this offers for the future of translation and communication on a global scale, isn’t it?
The Future of Professional Translation
While progress in machine translation was relatively slow and painstaking for several decades, the situation has changed dramatically in the last five years thanks to AI. Technological advances are improving exponentially, allowing new applications of machine translation to emerge.
As NMT improves, even in just a few years, professional translators can expect more powerful CAT tools that will allow them to work more efficiently and deliver faster, higher-quality translations.
The advent of AI and NMT technology has sparked widespread concern about the future of professional translators. If you’d like to read more about this, we delve deeper into this topic here. However, it’s worth noting that these technologies will never completely replace professional translators, as they cannot replicate the fluency and nuance of human language.
On the contrary, these technologies have the potential to free translators from routine and tedious tasks, allowing them to focus on more creative and value-added ones, such as:
linguistic revision and proofreading,
writing texts or copywriting,
business development, such as client acquisition, etc.
Ultimately, the future of translators presents a vast range of possibilities. The expertise of a professional translator is even more valuable and priceless than ever with the advent of AI and technological advances in the language industry.
What do you think about the future of machine translation? Will this technology continue to advance rapidly, or will we soon reach its limits? What about the translation profession? We’re curious to hear your take! Comment below and share your thoughts on this fascinating topic.
A sequence of logical instructions to solve a problem or perform a task.
Artificial Intelligence (AI)
A technology that uses Machine Learning algorithms to “learn” to translate text autonomously, capable of understanding the meaning of a text for more accurate and consistent translations.
Artificial neural networks
Machine Learning algorithms are inspired by the functioning of neurons in the human brain. They are used in many fields, including image recognition, machine translation, and sequence prediction.
A subcategory of machine translation that uses AI and Machine Learning techniques to analyze and understand the source and target languages to provide a translation.
Computer-Assisted Translation (CAT)
A translation method that uses software to help human translators translate faster and more accurately and manage translation projects, including translation memories and terminology databases.
Corpora (plural for corpus)
A set of text or language data collected for linguistic analysis, machine translation, or other similar applications.
A branch of Machine Learning that uses artificial neural networks with multiple layers to perform complex tasks, such as voice recognition and machine translation.
A method of data analysis that allows computers to learn from data without being explicitly programmed.
An old technology based on predefined rules to translate texts often limited to relatively simple translations.
Neural Machine Translation (NMT)
A technology that uses Deep Learning algorithms to translate text, capable of understanding the meaning of the text to produce more accurate and consistent translations.
A form of data representation that uses semantic concepts and relationships, in other words, it’s a way to represent the meaning of words or phrases in a way that is understandable to a machine.
Statistical machine translation
A method that uses statistics to determine the best matches based on patterns found in bilingual texts.
A set of data provided as an example for the neural network to learn from and train a Machine or Deep Learning algorithm.