Machine Translation: What is it, and How it Works
There was a time when people used bilingual dictionaries to translate one language into another. Now, technology is paving new pathways in the modern world; machine translation is replacing the traditional methods of language conversion. Many great names in the tech industry, such as Google and Microsoft, provide online language translation tools that automatically convert text into multiple languages in no time. According to Statista, the global machine translation market was recorded at $1060 million in 2022 and will cross $1500 million by 2024 due to increasing demands for content localisation. Aside from individual use, machine translation is an essential tool for businesses to expand their reach across different regions globally. The need for text conversion tools is increasing in the modern business industry.
What is Machine Translation?
Machine translation, also referred to as MT, is an automated language translation process that requires zero human effort. In this operation, the machine uses modern technologies like artificial intelligence and machine learning to translate text from one language to any targeted language in seconds. It uses various algorithms and patterns to provide efficient results. New tools can detect the language automatically using innovative text recognition beyond the simple word-to-word translation. Many businesses take advantage of machine translation to expand their offerings in different languages.
How does Machine Translation work?
Machine translation is an automated process in which computer software takes the input as text in any language. Different methods use artificial intelligence and machine learning to produce an outcome in the form of another language desired by the user. Here is a brief look at the automatic translation process.
The source text divides the text into smaller chunks called phrases. Each phrase is then searched in the original repository to find matches. The system uses machine learning and artificial intelligence to compare text in the database. After a suitable match is found, the method returns the output in the form of the translated text phrases. The phrases are then recompiled to form a sentence for the user as the final output. However, the complete lifecycle of processing data is complex and uses four main translation methods to produce comprehensible results.
As the name suggests, the rule-based machine translation approach applies to specific language rules and vocabularies created by experts. These standards cover the standard dictionaries, grammar rules, and semantic language patterns needed to deliver the appropriate translations from the source text to the target content. The library of lexicons can be adjusted over time to improve the translation quality further. The machine parses the text using a rule-based approach to create a transitional representation. As it relies on lexicons, this method is for basic situations for quick meanings of short phrases.
The statistical approach of machine translation has nothing to do with the language rules. This method does not know grammatical standards but uses algorithms that reference and analyse the previously translated text. It explores the millions of possibilities for ordering texts together. The result is based on database analysis extracting translations based on statistical likelihood. For instance, the method determines the phrase of the input source to be the exact phrase in another, mainly the target language. In simpler terms, this approach relies on analysing enormous human translation data to find the target segment’s closest analog.
The neural method of machine translation learns from translation and other resources that language experts approve. It analyses an extensive network of resources using neural network technology and artificial intelligence to get the job done. Most times, it combines statistical machine translation methods to process the data by applying AI algorithms. The neural translation approach encodes each phrase into machine code to generate a resulting target sentence. It is one of the highly complicated machine translation approaches that is also the most efficient in modern-day systems. Many IT service providers use a neural translation approach to aid external and internal communication within organisations.
A hybrid machine translation approach is usually a combination of statistical and neural methods applied simultaneously. Firms use this approach to improve the accuracy of machine translation. It is a fail-safe way of driving precision and ensuring control over the data processing instead of relying on only a single solution. This method utilises different translation combinations using the above mentioned approaches for accurate text conversions. A system built on a hybrid machine translation foundation can have multiple approaches within one processing engine. This approach is quite resource-hungry compared to other translation methods, as it involves multiple technologies to process the input.
Human vs. Machine Translation
As technology progresses, many operations are getting automated in several ways. The AI revolution is taking over simple human tasks, and recent technologies have improved the way it works. It is always an endless debate regarding human vs. machine translation because converting languages with 100% accuracy is a difficult task for developers. But no one can deny the fact that machine translations are becoming increasingly popular, not only with organisations but also with language service providers. Here are some significant areas where machine translation beats human intelligence.
Human translations are generally slower compared to machine translations. Humans need more time to process information and convert it to a different language. On the other hand, machines can process large text inputs in a matter of seconds to generate translated text output.
On average, a professional translator knows around three languages, while modern machine translation tools can translate more than 100 languages into one they may choose.
Linguists charge hefty amounts to translate different languages, while machine translators such as Google Translate and Microsoft Translator are mostly free.
How PF’s AI Powered ML can help Your Business Scale?
Machine translation is an evolving solution for translating the text into different languages. Many organisations use this technique to expand their products and services to other regions. The operation of processing data is carried out using four different approaches, among which the neural translation method performs best for many enterprise software applications.
Over the years, machine translation has helped businesses create real-time chatbot agents, provided decision support, customer recommendation engines and prevent fraud. With proper utilisation, machine translation engines can produce effective results that benefit both organisations and customers.
Reach out to us if you need any help automating your ML processes.