Neural machine translation (NMT) is already radically changing industries around the globe, and it’s just getting started. Currently, neural translation systems produce more accurate translations than traditional machine methods. Plus, they sound much more natural and human.
Researchers have been waiting for this moment since the 1940s. As neural network architectures expand, we’ll see continuous improvements and applications of NMT for years to come.
If you’re wondering how your business can benefit from NMT, you’ve come to the right place. So start here and move forward. The future is just around the corner.
Neural machine translation (NMT) is a neural network that uses artificial intelligence to translate text from one language to another. While neural network translation is complex, the basic premise of this technology is easier to understand if you think of these systems as human brains.
Neural networks are composed of synapses and neurons, allowing machines to learn, understand, and interpret multiple languages. Each neural network uses different processes, similar to the way humans learn and think in different ways.
NMT systems are trained using large amounts of data, which consists of translations completed by human translators. These neural translation systems learn from the data and generate translations, more accurate than some human translations.
Scientists, researchers, and language enthusiasts have long awaited the arrival of neural machine translation. Over many years, they’ve worked diligently to address problems with traditional machine translation.
In the past, we relied on SMT. In metaphorical terms, early statistical translations were closer to a cardboard cutout of their creators’ vision. Now, neural translation promises something more akin to human translation.
NMT learns the nuances and complexities of human language, which is something statistical machine translation could never do. Neural networks can also put source material into context. Because of this, they can generate more accurate, natural-sounding translations.
Although SMT has improved over the years, it has not seen the rapid expansion of NMT. However, some language service providers are now combining statistical translation with neural translation for additional quality control. This hybrid machine translation workflow will evolve further as more and more data is fed into NMT systems, increasing overall accuracy.
Neural network architecture is the fundamental structure of machine language learning translation. It is a processing system composed of neurons and synapses, which function like the human brain.
There are many different types of neural networks explicitly made for machine translation. The most popular include recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs).
There are many different types of neural networks, each with its strengths and weaknesses. So some networks are better suited for specific tasks than others.
For example, a recurrent neural network (RNN) is well-suited for tasks requiring the network to remember information over long periods. This is due to their ability to maintain internal state vectors, similar to our memory, which allows them to recall previous inputs. Some applications of RNNs include speech recognition and natural language processing.
On the other hand, a convolutional neural network (CNN) is better suited for tasks that require the network to process input images. CNNs extract features from images by scanning them like a human eye. According to Meta, CNN holds “the potential to scale translation and cover more of the world’s 6,500 languages.”
In general, there is no one-size-fits-all neural network. Instead, the best neural network for your business depends on the specific details of your project.
NMT systems are trained with large amounts of parallel data. Think of this data as examples or lessons designed to teach the neural machine. The encoder learns to read input sequences and produce corresponding output sequences. When the NMT system is trained, it’s ready for inference, the process of translating new text data.
Next, the encoder takes a sentence in the source language and transforms it into a representation vector, a sequence of numbers. Then, the decoder transforms those numbers into the corresponding words of the new target language and generates an output sequence.
Experts created attention mechanisms to improve the accuracy and reliability of machine learning translation. You can think of this much like your attention. It allows the encoder and decoder to focus on the most relevant parts of the source text when translating to the target language.
Many different output layers can be used, such as a softmax layer or a linear layer. The output layer of an NMT system is responsible for considering all the possible outputs. It then selects the most likely answer as the final translation.
Evaluation metrics measure the quality of translations produced by the NMT system. The most popular evaluation metric is the BLEU score. More on this below.
Neural translation has a wide variety of applications in business. From marketing content to technical translations, the increased accuracy makes neural translation an innovative solution for many industries and problems.
For most businesses having a human translate large volumes of texts is out of the question due to budget and time constraints. However, machine learning language translation offers you fast delivery at a fraction of the price.
Tech companies can use neural network systems for app and software translation. These advanced systems are much more sophisticated than in the past.
For example, Tomedes offers full-service, collaborative platforms featuring project management, software testing, and other innovative localization solutions. These features make it an ideal neural machine translator for tech startups in the gaming, software, and app sectors.
NMT can also be useful for medical translations and legal translation. However, these translations should be subject to a higher degree of scrutiny than other translations. Errors in medical or legal translations can have serious consequences. As such, it is vital to ensure the proper configuration of the NMT system.
For mission-critical translation projects, you should partner with a reputable machine language service provider that offers post-editing services in your industry. As an extra safety measure, their human translators can ensure your translation is free of errors.
NMT can also accurately translate websites, preserving the original text's meaning. You can connect with international customers or target multiple languages. A full-service LSP can also provide SEO keyword research and email marketing in the target language.
From electronics companies to toy manufacturers, every industry has technical documentation nowadays. Many businesses use NMT to translate user guides, manuals, specifications, and instructions. NMT can handle complex and technical language very well.
Marketers and businesses can use NMT to translate UX and market research documents, such as surveys or customer reviews. Sentiment analysis helps you understand the overall sentiment of a text. Additionally, NMT helps identify key phrases or terms for your market research.
NMT systems are typically deployed as web services, where users can submit source text and receive translated target text. Some of the best free online translators offering NMT services include Google Translate, Microsoft Translator, and DeepL.
With Google Translate, you can easily translate documents, notes, and content. It is a free offline and online machine translation tool. All you have to do is upload or hit translate. You can integrate Google Translate on your browser and mobile for quick, easy access wherever you go. As Google’s neural network expands, this translator will continue to increase in accuracy.
Bing Microsoft Translator is a free cloud service translator compatible with all Windows and Microsoft applications. This neural translator offers 105 languages, as well as real-time translation and image translation. While it’s very user-friendly, please remember all new tech is prone to errors.
DeepL is much smaller than the other big-name competitors, offering just 26 languages. However, users love this neural translator for its ease of use. Unfortunately, although it delivers accurate translations, it also can produce errors. So, make sure professionals complete post-editing.
Tomedes has served Fortune 500 companies, startups, and small businesses with cutting-edge neural machine translation and a network of over 20,000 language experts spanning over 120 languages. They also offer comprehensive machine translation post-editing services.
If you want to localize your business, you can’t go wrong with their collaborative platform and globalization solutions.
Systran software translates over 55 languages. They offer two main features for neural machine translation. First, their web-based interface allows users to translate easily from one language to another. Their API allows developers to integrate neural machine translation into their applications.
MemoQ offers neural machine translation in over 100 languages, using several machine translation systems. So you can get accurate translations of your documents, no matter the source language. MemoQ also offers human translation services. Just choose the right option for your needs.
In 2020, experts estimated neural machine translation had an accuracy of 60%-90%. Then, in an article last year, “Lionbridge’s R&D teams estimate that Neural Machine Translation is improving by 3-7% every year."
When you average the above numbers and account for the improvements, neural machine translation has around a 70% to 99% accuracy rate.
However, the accuracy of a neural machine translation system is better determined by the BLEU score. The accuracy of the system's translations is compared to human-generated translations to calculate the BLEU score. The closer the match, the higher the score. Other popular metrics include METEOR and NIST.
First, one of the most crucial quality factors is the size and complexity of the dataset used to train the system. If the system is trained on a small or simple dataset, its accuracy will be lower than a system trained on a large or complex dataset. Additionally, the system's quality may also decline over time as new data gets added to the training set.
You must choose the decoding algorithm wisely if you’re building your own NMT. The most popular decoding algorithm is beam search, but other algorithms are available as well. The choice of algorithm can have a significant impact on the accuracy of the system.
It is also essential to consider the attention mechanisms used by the system. Attention mechanisms help the system focus on the most relevant parts of the source text when translating into the target language. Without attention mechanisms, the system may produce inaccurate or unnatural translations.
You don’t have to look very far to see the power of machine learning translation. The world is globalizing before our eyes. What’s next is up to all of us. Neural translation will soon allow us to communicate regardless of language barriers.
It could change everything or nothing at all. That depends on your perspective.
No algorithm can predict the future, at least not yet. That’s what it means to be human and fallible. We wake up each day and face the unknown, hoping to find those small good things like joy and wonder. Then, there are times when we discover something beyond our comprehension.
Machine learning is one of those things. It’s reshaping our world in ways that we never thought possible. We are on the cusp of a new era where machines will learn and evolve at an accelerating pace. It’s up to us to decide how to use this power.
The future is machine learning. What will you do with it?