What is 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.

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:

1. Decode the source language meaning of the original text
2. 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:

1. The machine translation software parses the input text and creates a transitional representation
2. 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.

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

Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. You can localize content such as websites and applications for your diverse users, easily translate large volumes of text for analysis, and efficiently enable cross-lingual communication between users. With Amazon Translate, you can:

• Easily integrate into your applications with a simple API call

• Customize your machine-translated output with custom terminology to define how your brand names, model names, and other unique terms get translated

• Scale to meet your translation needs with fast and reliable translations

To get started with Amazon Translate, create an AWS account today.