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Language data annotation in Multilingual AI

Language data annotation in Multilingual AI

비즈니스 성장에 도움이 필요하십니까?

Can you speak Korean? If no, the above sentence was easily incomprehensible to you or to the English-trained AI on your website. As a business with a fair share of Korean-speaking customers, this could sure be a problem for your business, wouldn’t it?

 

What does Language data annotation mean?

Language annotation means that when data is available in many different forms, it is annotated or tagged to make it easy to read and comprehend for machine learning. The data that could be available in text or audio form is then tagged with additional notes and metadata. This annotation when applied to the entire document, can make it easily comprehensible through AI-based model developments.

The language data must be annotated with extreme precaution by fully understanding the meaning of the text in the document. Each sentence and phrase must be read closely by the annotator or it could result in misinterpretation of the data which could by consequence affect the machine learning output. Thus, understanding the importance of hiring a professional service to do the same when translating your business is paramount. Google Translate is yet another example of how despite being more efficient for personal use, it isn’t something a business can use to grow.

 

The Age of the Multilingual AI

It was only time that there would arise a need for our machines to become multilingual to accommodate the interest of the masses as well. Every business that has a multilingual customer base understands the importance of being able to communicate with them effectively. Multilingual dimensions are truly the next step for Artificial Intelligence at this rate.

Chatbots, for example, is one of the primary customer service channels. With many tech companies like Microsoft, Facebook, and Google evolving to adapting their AI in multiple languages, we become aware of the rising need for the same.

 

Language data annotation in AI?

Artificial intelligence algorithms are created by human-annotated datasets. Data annotation forms the very core of machine learning and can make or break AI as we know it. Thus, when introduced a multilingual aspect to machine learning, yet again data annotation is what can determine its success. Many companies often use open-source and easily available data of the internet to widen their dataset. However, this is not a very helpful strategy to those that are looking for an efficient and qualitative solution. When a brand decided to translate their business and implement localization in their usual marketing and service-providing communication, it is of utmost importance that it is done well. Language data annotation is an essential step in translating the AI to multiple languages effectively, which if not done professionally can result in multiple instances of miscommunication which will most certainly harm your relationship with your customers.

 

Outsourcing annotation services

Finding capable annotators can be rather difficult. Along with the task of testing, onboarding, distribution, and other management tasks, it cannot be easy to get a good job done. As a result, most tech companies make the smart choice of outsourcing this service from professionals that have established an effective system to help annotate your data precisely. By utilizing experienced players for such tasks, you can focus on what you do best, growing your brand!

 

How ActiveLoc can help

With ActiveLoc data annotation services, you can be assured about your data being handled professionally. With a high-quality and secured training data set compliant with the most stringent regulations, your brand can have more than just simple data labeling, text annotation, or tagging activities. The professional annotators and data specialists at ActiveLoc are trained with accurate metadata labeling and semantic tagging which can truly enhance your capabilities and produce outputs with precision. Get in touch with us to grow your business in the right direction!

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