How AI Translators Remove Language Barriers

Language barriers in communication are complex, but AI translators can help break them down. They empower individuals to connect, regardless of their linguistic ability.

They can also provide real-time translations for customer support agents, helping to reduce resolution times. It means customers aren’t put on hold while they wait to speak to someone who can understand them.

Natural Language Processing (NLP)

A subfield of AI that allows machines to read and understand human language. It will enable computers to interpret text and speech, recognize vocabulary, understand context, and measure sentiment. NLP is a potent tool for businesses because it allows communication across language boundaries and with a wider audience.

It is used in customer support, sales, marketing, and more, making it easier for companies to provide a seamless, cross-cultural experience. AI can help translate content and deliver inclusive language to your audiences. To ensure the message is understood across cultures and languages, it might automatically choose the appropriate term or phrase while speaking with a consumer.

The world’s most widely spoken languages—like English—are relatively easy for AI to translate. But rare languages present a bigger challenge.  Whether you want to enhance your team’s collaboration or boost your customer’s experiences, an AI language translator is the answer. 

Neural Machine Translation (NMT)

Neural Machine Translation (NMT) is a new type of MT that uses neural networks to translate text. It is more accurate than other statistical-based MT models and can learn from text like a human. It also uses contextual information to improve its output.

For example, it can recognize words often used in a sentence and identify the best translations. It can also detect grammatical errors in texts and adjust accordingly. While NMT has made major strides in improving translation quality, it still needs to catch up to the proficiency of skilled human translators. One of the biggest challenges is the NMT’s tendency to prioritize fluency over accuracy, leading to inaccurate or awkward-sounding translations.

Another challenge is that NMT systems need large amounts of training data to produce high-quality translations. Sourcing, collecting, and cleaning this data is an enormous undertaking that can take years to complete. Despite these challenges, NMT can transform how we communicate across languages.

With its speed, accuracy, and scalability, it has many applications, from enhancing the customer experience with a bilingual live chat agent to translating business communications globally. Using NMT can allow businesses to expand their reach, increase revenue, and open up new markets without requiring extensive human resources.

Machine Translation (MT)

Machine translation is using software to convert a text or speech into another language automatically. It is also known as Statistical MT and Rule-Based MT. Unlike human translators, MT can translate large quantities of texts and vocabulary quickly and accurately. It is the most widely used language technology and has been around for decades. However, it’s not without its drawbacks.

Due to its reliance on data and algorithms, MT is susceptible to inherent biases that may influence the results. It could include gender, regional, and racial preferences. Choosing an MT provider committed to responsible and ethical use of data is important. The latest development in AI translation is neural machine translation (NMT).

NMT is the most advanced iteration of artificial intelligence and uses various machine learning technologies to achieve a more natural and fluent translation. You’ve likely seen the technology in apps like Google Translate, which can translate text by hovering over it with your phone’s camera.

Generative AI translation considers context to provide accurate and relevant text that is on-brand for your brand. It is achieved by prompting the system with context, such as tone of voice, industry, audience, and more, so your content is always on-brand. Smartling also helps users manage their localization projects by providing workflow reports that analyze the quality of their translated content and suggest improvements.

Deep Learning

With generative AI bringing more accuracy and real-time communication to language translation, the days of using Google Translate or Bing may be numbered. While these free tools can provide basic translations, they cannot capture cultural nuances or idiomatic expressions. To deliver accurate and precise translations, neural machine translation technology requires massive training data sets. Sourcing, collecting, and cleaning that data is a huge undertaking.

Until recently, it’s been necessary to supplement machine translation with human input. However, new technologies like ML and NMT have enabled automated, scalable language translation that anyone can use. This translation technology can help organizations reach more customers by allowing them to communicate with people across languages and cultures.

For example, a customer support team can use an AI translator to communicate with clients in their native language. It can reduce resolution times and create a more positive customer experience. Language barriers are inconvenient for global customers and can limit information exchange, collaboration and overall business performance. By incorporating inclusive language into their communication processes, businesses can help to bridge these barriers and improve employee morale and business results.