What Is Machine Translation? How It Works and Applications

700 languages and 300 writing systems; machine translation is bigger than you think.

Machine translation has simplified communication and collaboration across several industries by evaluating the variety in accents and vocabulary of regional languages.

Machine translation uses artificial neural network software, which analyzes sequential language text, images, or audio and translates the language.

Whether it is transcribing legal formalities, generating video subtitles or targeting new demographics for a new marketing campaign, machine translation can supercharge your linguistic workflows and make you a multilingual expert. 

This sophisticated technology has moved beyond simple word-to-word translation and now understands grammar and meaning in order to interpret intent from one language to another.

Machine translation is being implemented at scale across diverse healthcare, legal, banking, finance and IT companies. Not only does it localize content in a custom language, but it also converts speech data or video data to new text for increased comprehensibility.

Machine translation types

In machine translation, two core components must work together: the input language,  that algorithm encodes, and the target language it decodes. Several options exist to implement this process. 

Statistical Machine Translation (SMT)

As machine learning was developing in the early 2000s, statistical translation began to take place. Now, these specialized tools are trained on large bilingual datasets and follow statistical modeling techniques to understand data distribution and map the input to a possible category. 

This method is based on probabilities and likelihoods and uses machine learning algorithms to determine translations as per previous ones. However, statistical translation can result in inaccurate output as the machine can’t always understand contextual clues and meanings in the original text and it translates whole phrases rather than individual words.

The prime example of statistical machine translation is Google Translate, that was launched in 2016 and derived insights from input queries with statistical analysis to predict the output in a different language.

Rule-based Machine Translation (RBMT)

The original form of machine-based translation, this technique dates back to the 1950s when human translators had to manually input multiple language dictionaries and grammatical structures into computers to attempt accurate translation.

Rule-based translation stems from specific linguistic and grammatical rules outlined in both the input and output language to translate words. Individual words are matched to each other as an exact-match equivalent, which can make it more difficult to create a meaningful output. 

While you can review the output for grammar and context using this technique, you miss out on nuances. Most of these translations need human edits made before use.

Neural Machine translation

Thanks to developments in AI, neural network translation has now become the standard technique for this type of work. Neural machine translation often integrates with deep learning tools that possesses the capabilities of handling long range dependencies. It involves neural networks like seq2seq models, transformer models or even recurrent neural networks to derive weights from sentences rather than isolated words and use "hidden state" to predict new content.

This type of machine translation requires little human intervention once the machine completes deep learning training. It also adds new languages as close to human understanding as possible.

Despite the high accuracy score that deep neural network translations offer, there’s still some human oversight needed to edit the output. So, this technique is not quite at the point where it can completely replace a human translator.

Hybrid machine translation

The hybrid method makes use of multiple techniques at once to generate an accurate machine translation. This approach usually begins with either rule-based or statistical machine translation, before the secondary translator. 

As an attempt to overcome the inaccuracies that a single source can create, this fine-tuning approach works well with limited resources. However, a lot of businesses have responded to the accuracy issues by using neural network machine translations for a reliable alternative as it requires only one tool.

How modern machine translation works

Deep learning machine translation calls for computers to work with a neural network that replicates the human brain as closely as possible. But how does that happen?

The input language passes through several interconnected nodes to generate the translated output. Rather than breaking up individual words for translation, the computer looks at each sentence as a whole to grasp the full context and meaning before translating the text. 

As a more recent development in AI technology,  large language models (LLMs) are the neural networks that can understand patterns in language and use them to derive  context of a written or spoken sentence. 

A technique known as “self-attention” lets these tools concentrate on individual parts of a sentence, while still taking into account the overall meaning of the sentence. These modern translation tools assess individual words and how they relate to each other and the full sentence before translating it into the output language.

Machine translation key components 

The internal architecture of machine translation tools is dependent on the probability distribution of categories for input and output data. It combines the derived value, time steps of words and pre-trained datasets to arrive at a conclusion for output. Here are the key components of a machine translation tool:

• Encoder-decoder architecture:  First, the machine learning tool converts input tokens into vectors and stores their value at a specific time step. This process is also known as positional embedding. The words till the position [t-1] are converted into vectors and stored against their occuring position in the sentence.
• Attention mechanism: "Attention" refers to the stress or prioritization given to specific words due to a more significant association with the output. Attention is enforced to follow the same sentence order or input protocol to keep the meaning of text intact after localizing it in a different language.
• Tokenization: In machine translation, sentences are broken into tokens and the sentiment of each token is analyzed to generate grammatically accurate sentences. It ensures that the output follows the same emotional and contextual ecosystem as the input.
• Training data: Whether labeled or unlabeled, the machine translation tools are trained with high quality training data to predict the right future category for input and match it with output category. Machine translation models are trained on large bilingual or multilingual datasets that contain pre-trained text pairs. 

Machine translation benefits

Advances in modern translation tools have added numerous benefits to this type of work, both for personal and business use.

• Greater accessibility. Machine translation removes language barriers worldwide. Many of these tools also translate text into multiple languages at once, which makes it possible for businesses or individuals to talk to multilingual audiences.  These machines also inadvertently benefit the visually impaired as text-to-speech allows users to vocalize actions.
• Reduced costs. The high speed and accuracy of machine translations mean that companies can translate anything for significantly less money than a human translator’s salary. Humans still need to edit and proof text, but machines have taken on much of the heavy lifting upfront, which saves money.
• Increased speed and volume. Machine translators can interpret millions of words and phrases instantly. It follows a "parallelism" technique to generate multiple samples of translations.
• Enhanced scalability. One of the biggest benefits of machine translation is that unsupervised learning continues every time a new translation is added to the machine, which means that translations are always improving. Autonomous learning means that translation outputs can be scaled as business needs change.

Machine translation challenges

While machine translation reduces the extra bit of time businesses need to give to translation services, it does have a few areas of limitations:

• Contextual ambiguity: As machine learning translation tools comprehend the meaning of a word or phrase based on it's frequency, it might analyze same words that are used in different contexts in a unidimensional way. 
• Idiomatic expressions:  Machine learning tools can not find suitable responses or labels for exclamatory or idiomatic expressions used in the input dataset. This leads to a noisy or inappropriately structured output. 
• Low-resource languages: Local dialects or native tongues of lightly populated countries aren't ideal for machine translation due to lack of training data. As the algorithm doesn't have enough data to scrape, it might not generate contextual responses.
• Syntax and grammar variations:  Different languages have their own grammatical rules or sentence constructions that won't be clear to the machine translation model. In these cases, the model might analyze them in one generic way. 
• Domain specific terminology: Technical fields like law, medicine or automotive might use domain-specific jargon or terms which the model can't translate accurately. 

Machine translation applications

For straightforward translations, like instructional text or simple conversations, businesses across industries have found meaningful applications.

Internal and external communications 

Companies with a dispersed workforce in many countries sometimes have a hard time sending company-wide communications. If language skills vary across offices, machine translation provides support for making sure everyone understands internal communications instantly. This simplifies everything from training materials and employee onboarding to company presentations and ceremonies.

External communications with customers, the press, or others carry the same importance. When businesses work internationally, a translator on hand at all times is incredibly helpful. Machine translation can translate existing content that the company has that is sent to new locations, such as blog posts or other marketing materials.

Regulated and restricted content 

Certain industries, like legal and healthcare, may be under strict regulations concerning who'll view the content. Having a human translator involved may require copious amounts of legal paperwork like non-disclosure agreements (NDAs) that can take time to draw up and finalize. When businesses face a time crunch, machine translation support can take some of the pressure off.

However, the machine has boundaries in terms of what it can ingest. Some confidential information is not disclosed to the machine for legal or ethical reasons. But in many cases, information in regulated industries, like patents, lawsuits, or drug warnings, is translated to other languages via machine translation.

Data analysis 

Machines can process data infinitely faster than the human brain can. As a result, companies who translate large amounts of text every day can use these tools to save significant time. 

For example, a travel company wants to gather data on sentiment analysis from the customers who have been on their tours. Using machine translation, the business can understand reviews and testimonials from customers all over the world to gain a better understanding of their thoughts or frustrations.

Customer service 

If your business has customers located across the globe, responding to them in their own language in a reasonable time is essential for building loyalty and trust with your audience. A machine translator works in real time for customer service problems and can automate many email and chat responses, which improves the customer experience without having to hire additional employees.

Content localization

With machine translation, you can design marketing materials, emails or advertisements in major international languages. These tools are powered with diverse regional datasets and offer high graphical computational power (GPU) to localize long threads of content in one go.

Localizing content workflows and tapping into additional audience communication can pass you off as a highly empathetic and inclusive company and reduce the requirement of any language intermediary.

Legal and healthcare

Machine translation is also pivotal in transcribing legal documents, summons, business letters or subpoenas into different languages for different clients. In the healthcare industry, machine translation can categorize patient records, derive critical insights from tests and translate diagnoses for different diseases.

In both of these sectors, machine translation can fine-tune administrative work and enable patients or clients to read the necessary documents or reports in their own language.These tools also use large language models (LLM) to add or optimize the content at any stage.

Education

Machine translation aids faster language learning by providing instant response in a different language to the student. It also empowers educators to design curriculums, assignments or exam papers in a regional language or convert written text into spoken audio that is easier to understand. This leads to improved focus and concentration in students that enables them to pass their assessments and go to the next level of language learning.

Por Favor, it wasn't so difficult.

Machines can’t yet replace human translators, but it can optimize the bulk of translation work in real time and in large volumes without additional costs. Make the most of what technology offers you: great efficiency and high-quality translations that these tools provide you.

Learn more about website translation tools and connect with your audience in the language you like.