Artificial intelligence for efficient support in translation work

Artificial Intelligence (AI) is becoming an ever more important part of our lives. Whether it is in our homes with smart speakers and automation or in the business world, its impact in our lives cannot be dismissed.

However, while the benefits of AI are obvious, in the past, using the technology with language translation was difficult, if not impossible. Language translation is an area that has always required human intervention. There’s simply too much nuance in language for a machine to understand without a lot of training, most often done painstakingly by hand.

In recent years, that situation has started to change. With new advances in Machine Learning (ML) along with the development of neural networks, this once-difficult task is now much more possible.

How AI can complement Humans in Language Translation

In many industries AI is feared as people believe that it’ll replace humans in their workplace. That fear has in recent years started to become somewhat ameliorated as people have started to realize that AI is better suited to a collaborative relationship. Likewise, this is the case in the translation industry, as with advances in AI, computers can now aid humans to a level that was once thought impossible.

AI and language translation are essential parts of the modern world. With access to the internet and information from around the world, we are no longer restricted to local knowledge. If there’s a need to communicate information in another language, we can easily find someone or something that will help us translate the words and phrases necessary for our goals.

Companies like Google are pioneering these new translation technologies and helping to create a new era of global communication and information access. In addition to on-the-fly translation of websites, smartphone apps are being developed and deployed that can help translate the spoken word. However, getting to this point hasn’t been without its challenges.

How to teach AI

Computer science has given us a way to train our machines autonomously. Artificial intelligence and translation are two major areas within computer sciences that have seen the most progress, with machine learning leading the way for artificial intelligence and deep learning paving the road for translation.

Machine learning is a common way computers learn, but artificial intelligence (AI) is what really drives them. AI simulates the structure and power of our brain by analyzing data and comprehending situations without experience.

Deep learning can be thought of as AI that simulates how humans learn and uses neural networks that match the function of our brains. This technology allows computers to identify their own mistakes and fix them automatically. Computers with deep learning use logic like humans do, while being better at translating languages.

Are Neural Networks the Future of the Translation Industry?

A lot of research has been done in both academia and industry to create new translation technologies. Neural networks have become increasingly popular as a training tool for machine translation engines, but how exactly do they work?

Neural networks are a form of machine learning where the network itself is trained to recognize patterns. Rather than being programmed with rules and instructions, it learns from data input through programming methods such as backpropagation. This allows neural networks to develop their own internal structure for translating words, rather than relying on predefined rules to dictate how they should operate.

This flexibility has allowed neural networks to be used in many different applications, including speech recognition, object recognition, and image classification. In addition to this open-ended use, neural network algorithms have also been developed specifically for translation purposes.

One of the benefits of neural machine translation is that once a system has been trained, it can transfer knowledge from one language to another. With multilayer perceptron models, input sequences had to be the same length as their resultant output sequence.

Improved model architectures for these models, through the use of recurrent neural networks organized into an encoder-decoder architecture, have allowed variable-length sequences in input and output, while adding attention mechanisms has improved translation with long sequences.

Modern neural networks can translate texts with a 60-90% level of accuracy. But these engines also occasionally make errors in practice that can affect the quality and usefulness of translated texts.

The encoder-decoder with attention model architecture described above is suited for sentence size at best. This becomes problematic when long paragraphs and documents of texts are translated in which the model doesn’t have enough context to understand the text.

How to Train Your Machine Translation Engine

AI requires training on massive amounts of data to be functional.

The more data, the better.

For example, a few months of courses at a university will yield much less knowledge than several decades of classes and life experience.

Machine translation systems are no different and require training over thousands of hours to reach an acceptable level.

Tip:

At clickworker, you can have suitable training data sets created to optimally train your AI system.

Datasets for Machine Learning

First, a foundational engine is essential for the production of a bespoke machine. Translation suppliers with years of experience have access to foundation engines that can help you build the translation machine you need for your language pairs and fields.

These foundational engines are dedicated to one or two languages, such as English-to-Japanese or German-to-English, for example.

The process of training machine translation systems can be more effective if the system has a large volume of high-quality data to start. Do-not-translate lists, style guides, and glossaries are also excellent resources when working with languages.

There are many factors that can affect training speed e.g. the neural network structure, the number of layers in the network, the size of each layer, and parameters for backpropagation optimization in a gradient descent approach.

How can deep learning change the translation industry?

The ability of a machine translation engine to accurately translate texts varies depending on the context. Currently, machine translation engines are generic and trained to translate assorted types of text, like recipes, menus, storyboards, chat log transcripts, and more. Like humans, machines cannot accurately translate text without context. This is the case when they do not know who is using the text and for what purpose.

Neural machine translation is getting better over time as new deep learning algorithms are being developed and neural technology architectures change. As this type of AI becomes more accurate, human translators will need to focus on tasks in which they excel, such as content editing or quality control reviews. These deep learning innovations will impact other areas where humans have the advantage, like speech recognition, image detection, or video analysis.