Artificial intelligence (AI) has significantly advanced human interaction with machines in recent years. AI has become a seamless part of our daily lives, from smart homes to self-driving cars. When applied to translating text or speech from one language to another, AI is helping to break down one of the biggest barriers between humans globally: language. 

Voice interactions are essential in many of these technological advances, especially in language translation. Here, AI enables instant translation across several mediums: text, voice, images, and even street signs. And now, AI can handle large amounts of text or speech that need that translation. 

The technology works by recognizing individual words and then leveraging similarities in how various languages express the relationships between those words. 

This translation capability is then packaged into a smartphone app, such as Google Translate, or integrated into a website, such as Facebook, for quick translation of text on the fly.

The applications of translation technology are important and impactful. They improve experiences such as being a customer with an international company (by improving understanding between company and customer) and facilitate the exchange of research and information. With AI translators easily available, traveling to another country where you don’t speak the language will become even easier. 
 
## Examples of language translation tools 

Many prominent technology firms have developed their own AI-driven language translation tools. Google Translate is probably the best-known, but Microsoft’s Translator app is a significant competitor.

[Facebook’s translation tool leverages convolutional neural networks]( https://readwrite.com/2019/11/02/machine-learning-for-translation-whats-the-state-of-the-language-art/) (CNNs), which are better than the more traditional recurrent neural networks (RNNs) for handling contextual aspects of language. 

In addition, many global projects now focus on improving and expanding AI-driven language translation, such as [the Masakhane project]( https://www.masakhane.io/), which builds translation models for African languages.

## Language translation techniques: how artificial translation works

Online language translation started in the 1990s with two major products: Babelfish by AltaVista and Systran by Xerox. Using statistical rules, these early web translation tools could handle short pieces of text. 

But now, we have to deal with vast volumes of data at a fast pace, which calls for a different approach. Today’s AI language translation tools leverage a deep learning technique called neural machine translation (NMT). 

Based on artificially created neural nets, this approach translates whole sentences rather than just individual words, making it faster and more accurate. With NMT, AI is able to learn from translations that have already been completed, picking up on word use, sentence structure, and intent based on context. This technique is much more effective than any technique used previously, as it requires less memory and data to perform well. All translations are connected, giving better context and accuracy to large volumes of speech or text. 

## Benefits of artificial intelligence in language translation

The use of artificial intelligence in language translation has many benefits. One is the ability to deliver instant results across a wide range of languages, for example, with Google Translate or Facebook’s inbuilt translation feature. 
 
These tools are integrated into the websites we use daily. They provide added convenience and a more streamlined experience when interacting with international products, services, and people. 

What’s more, language translation tools are usually free of charge and easily accessible to anyone with a computer or smartphone and an internet connection. [Many tools are now also available offline]( https://techcrunch.com/2018/04/18/microsoft-translator-gets-offline-ai-translations/?guccounter=1&amp;guce_referrer=aHR0cHM6Ly93d3cuZGlnaXRhbGlzdG1hZy5jb20vZGlnaXRhbC1lY29ub215LzIwMTgvMDcvMDYvYXJ0aWZpY2lhbC1pbnRlbGxpZ2VuY2UtaXMtY2hhbmdpbmctdHJhbnNsYXRpb24taW5kdXN0cnktYnV0LXdpbGwtaXQtd29yay0wNjE3ODY2MS8&amp;guce_referrer_sig=AQAAAK0sSjiUtMTXsIfgzlMqfriq5coSEGo3N-onnh_ln8mS8otbD0mDkCC8xiCsOvkjJuhHYpXMUdkkxk8UJ5AHEw1SuwPE4vT1Cpie65VzHMZYW7GKgTq3-ZEZBpg44EF6-4Y0ZCHcTTNTZS_zk_hLjFy6mtM70aTX5ZIaK6dILH9r), opening up new possibilities for traveling or doing business in areas where internet connections are less reliable. 

AI-driven language translation technology is advancing rapidly, constantly being improved in terms of speed and accuracy. But can it rival human translators? 

## Key challenges of AI-driven language translation 

Accuracy has long been one of the biggest concerns in language translation, and the world of AI is no different. In fact, it’s even more critical here. Deep learning, for all its perceived glamor, still has certain limitations. 

In an [interview with Wired Magazine]( https://www.wired.com/story/greedy-brittle-opaque-and-shallow-the-downsides-to-deep-learning/), Google researchers spoke candidly about some of these limitations. In particular, they pointed out that simply upscaling the neural net and adding more data doesn’t necessarily mean it can replicate human abilities. 

In the same article, NYU professor Gary Marcus described deep learning as “greedy, brittle, opaque, and shallow.” The ‘greed’ part of his statement refers to how neural nets demand enormous training data sets.  

Sourcing, gathering, and cleaning all that data is a significant challenge in itself, but it’s necessary. Better-quality data leads to better-quality translation models, which are essential for delivering accurate results for the end user. 

Another issue with AI-driven translation is the still unavoidable need to augment machine translation, at least to some extent, with human input. This situation has only [begun to change recently](https://venturebeat.com/2020/06/03/how-googleis-using-emerging-ai-techniques-to-improve-language-translation-quality/), with the human touch often necessary to achieve sufficient levels of accuracy. 

Translation of data-poor languages, such as Yoruba and Malayalam, presents yet another challenge – locating and gathering enough training data to satisfy a hungry neural net.  

## Towards better quality translation

Despite the knotty problems it still faces, the quality of AI-driven language translation has skyrocketed in recent years. But there are still ways for it to do better. Improving the quality of training data for AI is one of the most crucial factors here, such as through providing precisely annotated datasets.

Defined.ai’s proprietary translation workflows, combined with human translators, can source, translate, and validate machine translation datasets of bilingual pairs. You can use these datasets for language translation projects to train, improve, and validate baseline models, increasing accuracy in your chosen languages. Additionally, with our [Evaluation of Experience]( https://www.defined.ai/category/evaluation-of-experience) services, you can benchmark and assess the performance of your models, ensuring they meet user expectations and enhance the overall translation quality. This holistic approach not only improves existing language pairs but also enables expansion into new languages, broadening your translation reach.

Using artificial intelligence in language translation has incredible potential when you input only the best quality raw ingredients. 




How Artificial Intelligence is Breaking the Language Barrier