Business Translation And The Use of Artificial Intelligence

Google and other companies provide artificial intelligence (AI) to translate. You can access it from your smartphone. The translation is a more complex and extensive problem than most people realize. There are many unique and complex needs in the business community that make it difficult to provide accurate and reliable translations. AI is proving increasingly capable.

The Key To Business Language

The simple fact that every business sector has its own terms and phrases is one of the keys to business language. The accuracy needed by business translation services will not be achieved by a generic cloud translation system, which has been trained extensively through crowdsourcing and other public methods. The cloud is also a problem. Protecting intellectual property (IP) is a key goal for many businesses. They want their information to remain on-premises and behind their firewalls in order to do this.

Multilingual Businesses Face Unique Challenges

Multilingual businesses face unique challenges when it comes to understanding the company's data. Collaboration is another option. Almost everyone uses electronic communication, whether it's email and text messaging, or more formal chat systems. The ability to enhance these applications with instant translations can make a company's internal communications more effective and help drive growth. Industrial translation services also face a lot of challenges when it comes to enabling a multilingual business presence.

E-Discovery

JP Barazza (CIO, SYSTRAN) stated that e-discovery was a natural entry point to business translation with AI. Imagine global development teams in the high-tech and biotech industries. Strong translation can help oil and gas translation services become more efficient. Customer support is another area where translation can be of great benefit.

Artificial Intelligence is Only One Part of The Solution

Most companies use unsupervised learning, just like cloud-based models. It uses a much more carefully curated data set to train the systems for each industry. The logic of the system is not limited to the neural network. Due to the particular terminology used in many languages, procedural reasoning is used in pre-and post-processing around it to assist with understanding the business sector terminology. They are easier to manage for clearly defined linguistic conventions, while the neural network can handle the fluidity of the overall languages.

Names are used in American English and French French. This is an example of how rules are needed. The US uses a leader's name regularly, such as "President Biden" in the US. In France, news reports don't usually use names but use titles such as "The President of the United States." This is a two-way translation. Jean Senellart CEO, said that it is easy to translate English by dropping the name or expanding the title. What happens if the president changes and we add a name to translate from French to English?

The system would keep adding the name of the previous president until enough data was available to retrain it. "We decided to use the French reference style for translating to English to maintain accuracy." The use of explicit rules is a simple way to address this issue.

This combination of procedural rules and neural network gives the company flexibility. The core system can be trained with different plug-ins for different companies. This allows for a faster development process and easier updates. You don't have to retrain your deep learning system to add specific corporate or industry rules.

Different Business Translation

Business requires greater accuracy. Consumers are open to errors if they convey the main meaning through translations. Business translation services require accuracy. This is not only for compliance with regulations and contracts. A lack of accuracy can lead to product development delays, lower safety, or dissatisfied clients.

Due to this need for accuracy and the industry's current state, another component is needed. The automated systems are still not reliable enough to be trusted completely. The translations should be reviewed by humans working for agencies such as Industrial translation services.

The translation is complicated within the system and is therefore limited to a select group of languages. Therefore, pairwise engines are used. One engine can translate from English to French, while the other can translate from French to English.

Backpropagation is a strange method of training the systems. Backpropagation is used to correct errors and feed them back into the engine. Translating means that results are translated back through the second engine and then corrected. Although it's more complicated than that, one can understand the basic concepts of an interesting loop in which both engines train one another.

This is how translations are done now, but it is changing. This style results in a lot more engines, and the greater the number of languages, it means that there are many engines. To limit the number of engines, one solution is to use English as an intermediary language. This allows you to translate everything from English. This can lead to inefficiencies and errors. Hence, Professional translation services have to be careful.

Facebook recently released a single model which can be translated in all directions and for multiple languages. Individuals are more comfortable with mistakes so this is a great place for testing out such a model. However, the technology will eventually strengthen and translation companies will benefit.

Non-AI design problems are also driven by the business.

Wrapping Up

Due to the current state of AI-driven translation, which includes a lack of transparency in deep learning, companies will not be able to use it exclusively for business or government. It will be used in accordance with the 80/20 rule. The basic translation will save considerable time and effort, while human review and editing will be required for final business and government translations.

In the past decade, translation tools have seen great advancements. It is not surprising that the initial focus was on personal use due to the less strict requirements for translation between individuals. Technology has advanced to the point that it is possible to address more formal requirements for business and government translations. Although it is still early, the technology is looking promising. We will see that oil and gas translation services will get better with time.