AI’s Role in Translations
Artificial intelligence (AI) has become a significant part in translations. Get to know the current state of AI and machine translation (MT), its benefits and limitations, and the importance of human translations in combination with AI.
There has been a lot of hype around AI and machine translation for a while now and the terms are often used interchangeably.
Machine translation existed long before AI. The idea of using computers to automatically translate text first emerged in the early 1950s. Since then, the technology has developed from rule-based and statistical approaches to Neural Machine Translation (NMT).
NMT is a field of artificial intelligence that uses natural language processing and deep learning to understand the meaning of a given text and translate it from one language to another. Using deep neural networks, neural machine translation has made significant advancements in the recent past.
Today’s NMT engines are able to learn from huge amounts of bilingual text data, and can therefore handle more complex structures and have a higher accuracy rate than the previous techniques.
The most well-known machine translation systems in the market, such as Google Translate, DeepL and Bing Microsoft Translator, rely on the latest NMT technology.
AI-powered machine translation tools have come to the point where they are able to handle vast volumes of data and deliver fluent translations with impressive speed and a remarkable degree of accuracy.
Nevertheless, machine translation in its current technological state still has its limitations and can struggle with more complex language structures to accurately translate idiomatic expressions, cultural nuances and regional dialects. Another challenge of machine translation is to maintain consistency, this is because MT systems can have difficulties to capture the context of the source text.
The quality of the output of machine translation engines depends on several factors, including the language pair and the available training data. Moreover, not all MT systems are designed equally. Some are better than others for certain language combinations and vice versa.
Generally, machine translation performs very well with widely spoken languages where MT systems can rely on a huge amount of data, and between languages which are related with similar syntax and grammatical rules. Machine translation between the European languages for instance provides very good results. On the contrary, machine translation between languages which are contrasting, for instance English and Japanese, does not always work very well.
We have all heard about generative AI like ChatGPT (short for Chat Generative Pre-Trained Transformer) and how these Large Language Models (LLMs) can produce human-like responses to an incredible variety of questions and queries. How does it compare to neural machine translation?
While MT systems are specifically trained on explicit translation rules and bilingual text pairs to translate from one language to another, LLMs don’t rely on parallel repositories. This makes them more creative, particularly in scenarios where there is a lack of sufficient parallel data for a specific language pair or domain.
LLMs, such as ChatGPT, are pre-trained on massive amounts of text data, allowing them to acquire language knowledge and patterns from diverse sources. The pre-training phase equips these models with a broad understanding of grammar, syntax, and semantic relationships.
In terms of translations, ChatGPT achieves considerable results and is a useful tool in areas such as contextual understanding, but better translation quality is still provided by neural machine translation solutions.
In a nutshell, LLMs are highly fluent and quite accurate, while machine translation engines are very accurate and fairly fluent. However, both concepts need a human expert in the loop when reliable and error-free translations are required. As things stand now, MT systems are more proven and reliable than generative AI in translation. But the race is on.
MT systems can deliver instant translation in many languages, but as things stand the results aren’t always hundred percent accurate. Grammatical issues can arise and also inconsistency is very likely when translating longer texts.
Translations often require a level of empathy, knowledge, and experience that is difficult to replicate, when translating across languages and cultures. These scenarios need human expertise. This process whereby humans amend machine-generated translation, is known as post-editing. If you want the best mix of speed and quality, then Machine Translation and Post-Editing (MTPE) is the right choice.
We help to define the best MTPE strategies, and support the implementation of your customized solution, fully tailoring to your needs.
Keep your information confidential and secure throughout the translation process when using our technology.
We can machine translate all kinds of file types while keeping the original formatting of the source file.
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We can machine translate all kinds of file types and ensure that your information is kept confidential and secure throughout the translation process.
We help our customers with the decision of whether to use machine translation, and for which kinds of content types and language pairs. We support also the implementation of your customized machine translation solution.
How much post-editing is required depends on a number of different factors. Discover our Machine Translation Post-Editing (MTPE) Solutions and choose the best mix of speed and quality for your translation projects.