What Is Neural Machine Translation and How Does It Work?

Translation from one language to another has long been a challenging task, requiring human brain and numerous hours of labor. Recent advances in artificial intelligence (AI) have, however, given rise to a novel approach known as neural machine translation, which offers a promising solution to this difficulty.

But what is neural machine translation?

This article explores what neural machine translation is and how it works, including its advantages over traditional translation methods (such as Google Translate) and its wide-ranging applications.

Keep reading to learn:

• What is neural machine translation

• How neural machine translation works

• Advantages of neural machine translation systems

• Use cases of neural machine translation

• How to improve the accuracy of NMT

Pro tip: To effortlessly translate your content, leverage the power of neural machine translation coupled with a comprehensive translation management system, like Centus. Learn more.

What Is Neural Machine Translation?

Neural Machine Translation (NMT) is the use of artificial neural networks to automatically translate text from one language to another with remarkable accuracy .

Unlike Google translate (and other traditional translation techniques), which rely on rule-based or statistical approaches, neural machine translation uses deep learning algorithms to deliver more coherent and context-relevant translations.

Neural machine translation uses a neural network architecture designed specifically for translation-related tasks. The two main components of this architecture are an encoder and a decoder.

The encoder takes the source language sentence and converts it into a distributed representation known as a thought vector or context vector. This vector represents the semantic and grammatical content of the source sentence. The decoder then uses this input to generate the target language translation for this vector.

How Does Neural Machine Translation Work?

Neural machine translation uses artificial neural networks to translate text from one language to another. Unlike traditional techniques, which rely on statistical models and phrase-based translation procedures, NMT uses deep learning algorithms to produce more accurate and fluent translations.

Here's a closer look at how neural machine translation works:

Neural network architecture for neural machine translation

The core component of NMT is its neural network architecture, which consists of an encoder-decoder framework. The encoder processes the sentence input in the source language and converts it into a fixed-length vector form known as sentence embedding. This embedding captures the semantic meaning of the source sentence.

The decoder, on the other hand, uses the encoder's phrase embedding to generate the matching translation in the target language. The decoder is trained to produce the most accurate translation by making word-by-word predictions based on the words it has generated.

Training process of neural machine translation models

A large parallel corpus is required to train an NMT model. This corpus comprises sentence pairs from the source and destination languages.

Using it, the model improves its capacity to reduce the disparity between the reference translations in the training data and the predicted translations. The end result is the improved translation performance of the model.

Language models

The language model analyzes huge amounts of monolingual data to capture vocabulary, grammar, and sentence structure. The language model also helps in producing fluent and accurate translations.

It predicts the most likely output based on the input sentence, assigning probabilities to possible translations or phrases based on its grasp of the language. This serves as a guide for the translation process.

Recurrent neural networks (RNNs) and transformers are two common ways through which language model is implemented in neural machine translation. Recurrent neural networks process input sequentially and update hidden states to capture context, while transformers capture word dependencies through self-attention mechanisms.

The language model can be trained together with other translation components. It can also be pre-trained and fine-tuned using translation-specific data. This approach uses large-scale monolingual data to improve fluency and language understanding.

Encoder-decoder framework and attention mechanism

In neural machine translation, an attention mechanism can help optimize the encoder-decoder structure. This mechanism allows the model to focus on different parts of the source sentence during translation depending on how relevant they are to the current generated word.

By assigning appropriate weights to the various input pieces, the model can handle long sentences and improve the quality of translation.

Advantages of Neural Machine Translation

Using neural machine translation systems has several advantages over the human brain and traditional translation methods that have made it the go-to technique for many translation tasks.

Enhanced translation quality

A neural translation model can capture complex linguistic contexts and patterns due to their deep neural network architecture. As a result, grammatical errors are rare, and translations appear more natural.

The fluency and coherence of the translations generated by NMT models improve when they gain a thorough understanding of the relationships between languages through training on huge parallel corpora.

Contextual understanding

NMT models excel at capturing contextual information and determining the intended meaning of a statement. These models can leverage their neural network architecture and attention mechanisms to produce contextually appropriate translations that consider the entire source sentence.

Adaptability to multiple language pairs

Neural machine translation is a versatile, scalable, and adaptable solution for multilingual translation workloads.

Human translators and other traditional translation systems rely on language-specific rules, making them less adaptive when translating across multiple language pairs. NMT models, on the other hand, can be trained using a single, unified framework that can support a wide range of language pairs.

Applications of Neural Machine Translation

Neural machine translation systems have proven to perform amazingly well in different domains by leveraging the power of neural networks. Here are some of the key applications of neural machine translation:

Cross-border communication and global businesses

Effective communication across language barriers plays a key role in successful collaboration, negotiation, and expansion. Neural machine translation provides accurate and on-demand translation services, allowing for seamless cross-border communication.

Companies can use neural machine translation technology to communicate with international partners, reach a larger audience, and overcome language barriers, thereby encouraging global growth and opportunity.

Localization of software, websites, and multimedia content

When companies enter new markets, they must adapt their software, websites, and multimedia content to the local languages and cultures of the new markets.

NMT provides a powerful localization solution to ensure that the translated text properly conveys the intended message while also capturing the cultural references specific to the target audience.

NMT automates the translation process, thereby streamlining the localization workflow, saving time and money, and also maintaining consistency and quality.

Enhancing language learning and research

NMT systems allow language learners to access translated resources, practice conversations, and receive rapid feedback on their language proficiency.

Neural machine translation technology also assists researchers in analyzing and extracting data from multilingual sources, thereby allowing for the discovery of cross-lingual knowledge and the facilitation of cross-linguistic collaborations.

Explore other use cases and business applications of AI translation here.

How to Improve The Accuracy of Neural Machine Translation

Accuracy is an important element of any translation system, including NMT. Over the years, NMT has significantly advanced in ensuring translation accuracy, thereby surpassing different conventional methods. Some methods that led to these advancements include:

Deep learning techniques

NMT models use deep learning techniques such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers to recognize complex linguistic relationships and patterns.

Learning from a significant amount of training data allows these models to increase their prediction accuracy. By analyzing the input text across multiple layers of neural networks, NMT models may extract and convey the underlying semantic information, increasing translation accuracy.

Training with parallel corpora

Training NMT models with parallel corpora has significantly improved their accuracy. This training helps NMT models learn the relationships and alignments between words or phrases in different languages. The training process also helps the models to provide more accurate translations when they encounter new sentences.

Attention mechanism

The attention mechanism of the NMT is important for improving translation accuracy. It allows the model to focus on critical phrases in the original text.

The attention mechanism assists the model in allocating its resources correctly, resulting in more accurate translations. It accomplishes this by assigning different weights to different portions of the input sequence.

Pro tip: To manage a complex translation project, leverage the power of NMT along with a professional localization suite. With it, you can save time and achieve the highest quality of translations. Learn more.

To Sum Up

NMT is the next logical step in the development of traditional machine translation methods. However, unlike its predecessors, NMT leverages learning algorithms to create contextually-relevant translations with unparalleled accuracy.

NMT can effectively translate multiple language pairs, making it indispensable for global business, software localization, language learning and research.

FAQ

What languages support neural machine translation?

The number of languages handled by NMT systems is rapidly growing, and these systems are constantly improving. NMT currently supports Afrikaans, Arabic, Chinese (simplified and traditional), English, French, German, Hindi, Italian, Japanese, Korean, Russian, Spanish, and many other languages.

What is neural machine translation based on?

Neural machine translation is based on artificial neural networks. The neural networks can learn to perform complex translation tasks by analyzing large amounts of previously-translated content.

What are the 4 types of machine translation in NLP?

The four main types of machine translation (MT) in NLP are statistical machine translation (SMT), rule-based machine translation (RBMT), hybrid machine translation (HMT), and neural machine translation (NMT).

Statistical machine translation uses parallel text collections and statistical approaches for translation.

RBMT uses a set of predefined rules for translation, which are regularly customized for specific domains.

SMT is used for translation in HMT, while RBMT is used for error correction.

NMT uses artificial neural networks for translation and it has demonstrated superior performance when compared to SMT and RBMT.

What is neural machine translation used for?

Neural machine translation (NMT) enables real-time translation in international trade and partnerships, thereby facilitating cross-border communication. It makes it easier to localize software, websites, and multimedia content, ensuring linguistic and cultural relevance for target audiences.

Also, NMT systems improve language learning and allow academics to analyze multilingual data to gain new insights. Furthermore, NMT supports the creation of multilingual content, such as articles and social media postings, to reach a larger audience.