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Machine translation is the process of using artificial intelligence to automatically translate text from one language to another without human involvement. Modern machine translation goes beyond simple word-to-word translation to communicate the full meaning of the original language text in the target language. It analyzes all text elements and recognizes how the words influence one another.
What are the benefits of machine translation?
Human translators use machine translation services to translate faster and more efficiently. We give some benefits of machine translation below:
Automated translation assistance
Machine translation provides a good starting point for professional human translators. Many translation management systems integrate one or more machine translation models into their workflow. They have settings to run translations automatically, then send them to human translators for post-editing.
Speed and volume
Machine translation works very fast, translating millions of words almost instantaneously. It can translate large amounts of data, such as real-time chat or large-scale legal cases. It can also process documents in a foreign language, search for relevant terms, and remember those terms for future applications.
Large language selection
Many major machine translation providers offer support for 50-100+ languages. Translations also happen simultaneously for multiple languages, which is useful for global product rollouts and documentation updates.
Cost-effective translation
Machine translation increases productivity and the ability to deliver translations faster, reducing the time to market. There is less human involvement in the process as machine translation provides basic but valuable translations, reducing both the cost and time of delivery. For example, in high-volume projects, you can integrate machine translation with your content management systems to automatically tag and organize the content before translating it to different languages.
What are some use cases of machine translation?
here are several use cases of machine translation, such as those given below:
Internal communication
For a company operating in different countries across the world, communication can be difficult to manage. Language skills can vary from employee to employee, and some may not understand the company’s official language well enough. Machine translation helps to lower or eliminate the language barrier in communication. Individuals quickly obtain a translation of the text and understand the content’s core message. You can use it to translate presentations, company bulletins, and other common communication.
External communication
Companies use machine translation to communicate more efficiently with external stakeholders and customers. For instance, you can translate important documents into different languages for global partners and customers. If an online store operates in many different countries, machine translation can translate product reviews so customers can read them in their own language.
Data analysis
Some types of machine translation can process millions of user-generated comments and deliver highly accurate results in a short timeframe. Companies translate the large amount of content posted on social media and websites every day, and translate it for analytics. For example, they can automatically analyze customer opinions written in various languages.
Online customer service
With machine translation, brands can interact with customers all over the world, no matter what language they speak. For example, they can use machine translation to:
- Accurately translate requests from customers all over the world
- Increase the scale of live chat and automate customer service emails
- Improve the customer experience without hiring more employees
Legal research
The legal department uses machine translation for preparing legal documents in different countries. With machine translation, a large amount of content becomes available for analysis that would have been difficult to process in different languages.
What is the history of machine translation?
The idea of using computers to translate human languages automatically first emerged in the early 1950s. However, at the time, the complexity of translation was far higher than early estimates by computer scientists. It required enormous data processing power and storage, which was beyond the capabilities of early machines.
In the early 2000s, computer software, data, and hardware became capable of doing basic machine translation. Early developers used statistical databases of languages to train computers to translate text. This involved a lot of manual labor and time. Each added language required them to start over with the development for that language. Since then, machine translation has developed in speed and accuracy, and several different machine translation strategies have emerged.
What are the different approaches to machine translation?
In machine translation, the original text or language is called source language, and the language you want to translate it to is called the target language. Machine translation works by following a basic two-step process:
- Decode the source language meaning of the original text
- Encode the meaning into the target language
We give some common approaches on how language translation technology implements this machine translation process.
Rule-based machine translation
Language experts develop built-in linguistic rules and bilingual dictionaries for specific industries or topics. Rule-based machine translation uses these dictionaries to translate specific content accurately. The steps in the process are:
- The machine translation software parses the input text and creates a transitional representation
- It converts the representation into target language using the grammar rules and dictionaries as a reference
Pros and cons
Rule-based machine translation can be customized to a specific industry or topic. It is predictable and provides quality translation. However, it produces poor results if the source text has errors or uses words not present in the built-in dictionaries. The only way to improve it is by manually updating dictionaries regularly.
Statistical machine translation
Instead of relying on linguistic rules, statistical machine translation uses machine learning to translate text. The machine learning algorithms analyze large amounts of human translations that already exist and look for statistical patterns. The software then makes an intelligent guess when asked to translate a new source text. It makes predictions on the basis of the statistical likelihood that a specific word or phrase will be with another word or phrase in the target language.
Syntax-based machine translation
Syntax-based machine translation is a sub-category of statistical machine translation. It uses grammatical rules to translate syntactic units. It analyzes sentences to incorporate syntax rules into statistical translation models.
Pros and cons
Statistical methods require training on millions of words for every language pair. However, with sufficient data the machine translations are accurate.
Neural machine translation
Neural machine translation uses artificial intelligence to learn languages, and to continuously improve that knowledge using a specific machine learning method called neural networks. It often works in combination with statistical translation methods.
Neural network
A neural network is an interconnected set of nodes inspired by the human brain. It is an information system where input data passes through several interconnected nodes to generate an output. Neural machine translation software uses neural networks to work with enormous datasets. Each node makes one attributed change of source text to target text until the output node gives the final result.
Neural machine translation vs other translation methods
Neural networks consider the whole input sentence at each step when producing the output sentence, Other machine translation models break an input sentence into sets of words and phrases, mapping them to a word or sentence in the target language. Neural machine translation systems can address many limitations of other methods and often produce better quality translations.
Hybrid machine translation
Hybrid machine translation tools use two or more machine translation models on one piece of software. You can use the hybrid approach to improve the effectiveness of a single translation model. This machine translation process commonly uses rule-based and statistical machine translation subsystems. The final translation output is the combination of the output of all subsystems.
Pros and cons
Hybrid machine translation models successfully improve translation quality by overcoming the issues linked with single translation methods.
What is a computer-assisted translation tool?
Computer-assisted translation (CAT) tools work alongside machine translation software to support text translation. CAT tools automate translation-related tasks such as editing, managing, and storing translations. Text is inputted into the CAT software and divided into segments, such as phrases, sentences, or paragraphs. The software saves each segment and its translation in a database, speeding up the translation process and guaranteeing consistency with previous translations.
Many global companies use CAT software tools to automate projects that require translation.
Automated translation
Automated translation refers to any automation built into the CAT tool to carry out repetitive translation-related tasks. Automated translation works with triggers embedded in the text that tell the system to use automation. For example, you can use it to insert commonly used text into documents from a database.
What is the most accurate machine translation technology?
Neural machine translation is universally accepted as the most accurate, versatile, and fluent machine translation approach. Since its invention in the mid-2010s, neural machine translation has become the most advanced machine translation technology. It is more accurate than statistical machine translation, from fluency to generalization. It is now considered the standard in machine translation development.
The performance of a machine translator depends on several factors, including the:
- Machine translation engine or technology
- Language pair
- Available training data
- Text types for translation. As the software performs more translations for a specific language or domain, it will produce higher quality output. Once trained, neural machine translation becomes more accurate, faster, and easier to add languages
Can machine translation replace human translation?
Machine translation can replace human translation in a few instances where it makes sense and is required in high volumes. For example, many service-related companies use machine translation to help customers via an instant chat feature or quickly respond to emails. However, if you translate more in-depth content, such as web pages or mobile applications, the translation may be inaccurate. It is important to have a human translator edit the content before use.
