Multilingual NLP: How to Break Down Language Barriers in Global Business  

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The article explores how breaking language barriers is essential for international business presenсe, emphasizing the importance of multilingual Natural Language Processing (NLP). It highlights the challenges of language diversity in expanding markets and shows how multilingual NLP can overcome them. By focusing on clear communication, tailored content, and instant interactions, it demonstrates how multilingual NLP helps businesses thrive in the face of globalization. 

Challenges of Language Diversity in Global Business

E-commerce providers, marketplaces, brand providers, and manufacturers have experienced an unprecedented online boom in recent years. More and more companies rely on cross-border E-commerce or are now selling their offerings internationally as part of their digital transformation. As predicted, more than $3.3 trillion in cross-border transactions will be conducted in 2028. 

At the same time, providers from the USA and Asia are increasingly targeting European markets, as marketplaces such as Amazon, eBay, and Zalando offer first-class business opportunities across Europe. Therefore, there are many reasons why your business might need a website in different languages: 

  1. Global collaboration: Of course, offering a website in languages other than English – or in just one primary language – is important if your business wants to expand into new markets. Giving potential customers the cross-cultural communication opportunity to browse and navigate your website in their native language is an effective way to capture their interest and convert them into loyal customers. 
  2. Language localization: If you rely on E-commerce sales, ignore the language factor at your peril. It has been shown time and time again that website users are more likely to complete a purchase if the purchase process is in their own language. 35% said they would be willing to switch to another company that offers support in their native language. 
  3. Brand image and trust: Perhaps there is a large population in your service area that does not speak the language in which your website is written. To gain their trust and hopefully their favor, you could have the website translated into their native language, and language technology can be a compelling competitive advantage. 
  4. SEO improvement: From a business perspective, linguistic diversity on your website helps build your brand image, promotes search engine optimization, and sets you apart from your competitors. While search engine optimization can be complicated, investing in a new language strategy offers tremendous value. Basically, careful keyword research and providing relevant, high-quality content in new languages will strengthen your website’s SERPs in foreign language target markets. In other words, multilingual communication increases your likelihood of being noticed by your target group. 
  5. Differentiation from competitors: Similarly, your institution could offer information for refugees or minority groups in your region. Maybe you offer housing counseling, legal assistance, or educational services. Or maybe you want to attract tourists and need to communicate with them in their own language. 

Whether B2C or B2B, successful go-to-market strategies do not have to be complex. Many providers come together across Europe and the US, placing their offers across channels and in a personalized manner. However, regardless of the size of your business, a website that is available to your existing and future users in their preferred language is not just an option but a must. 

As companies expand their reach into international markets, they face a significant challenge: language barriers. These barriers can hinder collaboration, complicate negotiations, and limit market access. The most significant ones include: 

  • Language proficiency: Differences in language intelligence levels among team members can lead to misunderstandings and misinterpretations of information. For example, a marketing team member may misinterpret the meaning of a phrase in a foreign language, leading to incorrect messaging in an international advertising campaign. 
  • Cultural nuances: Cultural nuances embedded within languages can be challenging to navigate, leading to miscommunications. For instance, a direct communication style in one culture may be perceived as rude or confrontational in another, potentially causing tension during negotiations between international business partners. 
  • Idioms and expressions: Idioms and expressions unique to specific languages can be easily misunderstood or mistranslated, leading to confusion. For example, the English phrase «to let the cat out of the bag» may not have an equivalent expression in another language, failing to convey the intended message accurately and potentially affecting cultural sensitivity. 
  • Technical jargon: Technical jargon and industry-specific terminology may vary across languages, posing challenges for effective communication. In a business meeting discussing engineering specifications, misunderstanding technical terms due to language differences could result in errors in product development or project implementation. 

Moreover, even though English is widely spoken, the number of other languages in the digital universe is growing. English accounts for over 50% of languages used on the Internet, and the countries with the most Internet users largely have other official languages.   

How do businesses bridge these language barriers? Here’s where multilingual natural language processing (NLP) comes into play. 

Understanding Multilingual NLP

Multilingual NLP is not just a buzzword but a seismic change in natural language understanding. At its core, multilingual NLP models use AI algorithms to understand, interpret, and generate human speech – it goes beyond mere language translation. Multilingual NLP addresses the nuances of cultural context with idioms and dialects. It not only simplifies communication but also promotes closer customer relationships, which ultimately leads to growth and success in the global market. 

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Overcoming language barriers in the business world not only creates new business opportunities but also helps companies make the most of existing opportunities. It is estimated that 29% of companies have lost potential customers simply because they do not have multilingual customer service. Therefore, if the project or website is likely to contain texts in different languages, business executives must be careful about which NLP applications they use. The key components of multilingual NLP include: 

  • Machine learning algorithms: ML algorithms are utilized for tasks such as text classification, named entity recognition, and sentiment analysis across multiple languages. 
  • Natural Language Understanding (NLU): NLU systems process unstructured text data to derive meaning, enabling comprehension of user intent, sentiment, and context. 
  • Language models: Large language models, such as transformer-based architectures like BERT and GPT, learn language representations from vast amounts of text data, facilitating multilingual text processing and generation. 
  • Machine translation: Machine translation systems translate text from one language to another, leveraging neural machine translation models trained on bilingual communication. 
  • Speech recognition: Speech recognition technologies convert spoken language into text, enabling audio data processing for multilingual NLP tasks. 

With the development of machine learning and the increasing prevalence of AI software development services that help mimic human behavior, the way machines use language has become increasingly sophisticated. NLP algorithms process vast amounts of data and learn from them. They are no longer programmed to behave in a certain way but can «educate» themselves to a certain extent. Here’s how multilingual NLP works: 

  1. Data collection: Multilingual NLP models collect large volumes of text data in multiple languages from various sources, including websites, documents, and social media platforms. 
  2. Preprocessing: Text data undergoes preprocessing steps such as tokenization, stemming, and lemmatization to standardize and prepare it for further analysis. 
  3. Language identification: The language of each text document is identified using language detection techniques to ensure appropriate processing based on the language involved. 
  4. Feature extraction: Relevant features are extracted from the text data using techniques like word embeddings or contextual embeddings to capture semantic meaning and context. 
  5. Model training and evaluation: Machine learning models are trained on multilingual text data using techniques like transfer learning to build models capable of understanding and generating text in multiple languages. Models are evaluated on performance metrics such as accuracy, precision, and recall to ensure effective language support. 

Applications of Multilingual NLP in Business

When it comes to customer service, personalization is king. Multilingual NLP enables companies to deliver personalized experiences to their customers. Let’s examine the technology’s implementation in practice. 

Case 1: Language barrier in E-commerce customer support 

A multinational e-commerce platform struggled to provide personalized customer support due to language barriers. With customers from diverse linguistic backgrounds, the company faces challenges in understanding and addressing their inquiries effectively. Here’s how retailer addressed this solution: 

  • The company’s marketing department identified a growing need for multilingual support due to its diverse customer base. 
  • In response, the company researched AI technologies and sought guidance from tech providers offering NLP services to address language barriers effectively. 
  • Upon implementing AI-augmented chatbots, the company received positive feedback from customers appreciating the ability to interact in their preferred languages and receiving tailored support, resulting in increased satisfaction and loyalty. 

Case 2: Programmatic advertising optimization using AI 

A leading digital advertising agency struggled to optimize its programmatic advertising campaigns across various platforms due to the complexity of targeting diverse audiences effectively. The agency comprehensively analyzed past advertising campaigns and identified inefficiencies in audience targeting and ad placement. They found manual optimization processes were time-consuming and often resulted in suboptimal outcomes. 

Here’s what the scope of work included: 

  • Recognizing the need to leverage advanced technology, the agency explored solutions for programmatic advertising optimization using Artificial Intelligence (AI). They partnered with a tech firm specializing in AI-driven advertising solutions. 
  • The agency integrated AI-powered algorithms into its advertising platform, allowing for real-time ad placements and audience targeting optimization. This integration was gradual, with initial tests conducted on a small scale to assess effectiveness. 
  • Advertisers welcomed the improved performance of their campaigns, with increased click-through rates and conversions. The agency observed significant time savings and cost efficiencies due to the automation of optimization processes. 

Case 3: Multilingual communication in telemedicine during the COVID-19 outbreak 

During the pandemics, the healthcare provider experienced a surge in demand for telemedicine services, necessitating remote consultations with patients worldwide. The linguistic features in the context of communicating with patients under stress were important. Here’s how the company overcame the language barriers: 

  • The healthcare provider collaborated with technology experts to integrate multilingual NLP capabilities into their telemedicine platform. By leveraging NLP algorithms, the platform improved spoken and written communication between healthcare professionals and patients in real-time, ensuring clear and accurate communication despite language differences. Training sessions empowered employees to effectively communicate with patients in diverse languages during telemedicine consultations. 
  • Healthcare professionals experience improved efficiency and effectiveness in telemedicine consultations, as they can communicate seamlessly with patients in their native languages. Patients felt more comfortable and understood during consultations conducted in their preferred language. 
  • The healthcare provider observed increased utilization of telemedicine services among linguistically diverse patient groups, especially when in-person visits were limited. 

Challenges and Limitations

Multilingual NLP is extremely versatile and can be used for language recognition in numerous areas. Language technology solutions help interact with customers to process orders, collect data, and accept and resolve complaints. However, the complexity of the language, cultural sensitivity and the rapid pace of technological progress represent hurdles that companies must overcome. Some of them include: 

  • Compound nouns: Such nouns pose challenges in languages like German, where words are concatenated to form longer ones, impacting NLP systems. For instance, splitting «nuclear power plant» into «nuclear power + plant» or «core + power plant» requires careful consideration. One potential solution is to generate all possible splittings to avoid incorrect breakdowns. 
  • Cultural nuances: Context sensitivity in multilingual NLP refers to recognizing idiomatic expressions, understanding cultural references, and adapting language processing to account for cultural variations in communication styles and norms. 
  • Tokenization rules: Languages like Chinese and Japanese lack spaces between words, necessitating tokenization for NLP tasks. For example, in Chinese, «computer» is written without spaces as «计算机» («jìsuànjī»). Utilizing machine learning algorithms to detect word boundaries can aid in accurate tokenization. 
  • Different spelling variants: Spelling variations in English, such as between British and American English, can affect text analysis. For instance, «sanitize» in British English may be written as «sanitise» in American English. A potential solution is to normalize text to a standard spelling variant using rules and dictionaries, as done in the local spelling library. 
  • Misuse of data: NLP can spread false information under certain circumstances. This can lead people to believe fake news. Additionally, natural language processing models can inadvertently propagate biases and discriminatory statements if they are present in the training data. Data analytics services include thorough process reviews to ensure that NLP systems handle cultural nuances appropriately and address any potential biases or inaccuracies. 
  • Multiple writing systems: Languages like Serbian and Chinese have multiple writing systems, complicating NLP tasks. For example, Serbian can be written in both Latin and Cyrillic scripts. One solution is to normalize text to a canonical form using tools or APIs that handle conversions between writing systems, ensuring consistent processing in NLP systems.  
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Further development of this transformative technology holds the potential to transform entire industries and redefine the way businesses interact on a global scale. Let’s take a look at some of the exciting opportunities that lie ahead: 

  • Enhanced emotional recognition: Future advancements in multilingual NLP will enhance emotional recognition capabilities across languages, enabling chatbots to offer empathetic responses in real time. This trend may be augmented with advancements in affective computing and deep learning techniques. 
  • Real-time multilingual interactions: In the future, multilingual NLP will enable real-time interactions without language barriers, fostering seamless communication between individuals and companies. For example, a French-speaking customer effortlessly communicates with a Japanese-speaking customer service agent in real-time 
  • Precise understanding of complex documents: Future developments in multilingual NLP will enable a precise understanding of complex legal or medical documents, facilitating accurate summarization for professionals. This trend may leverage advancements in domain-specific language models and knowledge graph integration. 
  • Personalized video content generation: Advanced models in multilingual NLP will generate personalized video content based on individual preferences and behaviors, increasing engagement. This trend could be supported by innovations in natural language generation, computer vision, and user behavior analysis. 
  • Enhanced sentiment analysis precision: Future advancements in multilingual NLP will enhance sentiment analysis precision, which is crucial for predicting market developments and identifying opportunities across languages. This trend may benefit from improvements in machine learning models, fine-tuning techniques, and data augmentation strategies. 

Overall, the global reach of AI and multilingual NLP is not a destination, but a journey. It is a journey that holds the promise of a world united by words, where companies overcome language barriers and build bridges of understanding.


Communicating with the audience in their native language not only enhances content and product discoverability but also fosters deep personalization and potentially boosts conversion rates. By tailoring communication to the audience’s language preferences, online businesses can significantly enhance engagement and drive more conversions.  

ecision-makers outlined the following technology’s benefits: 

  • NLP eliminates the need for extensive translation services, resulting in significant cost savings for companies of all sizes. 
  • Companies can now communicate with their global audiences in real time, driving immediate connections and responses. 
  • By communicating effortlessly with customers worldwide, companies can confidently expand into new markets and reach previously untapped audiences. 
  • Multilingual NLP considers cultural nuances and ensures that messages are understandable and culturally appropriate, which is crucial for building trust. 

Given our experience in custom-built solutions, you can get the best foundation for advanced translation models based on deep learning to identify target groups and personalize content more effectively. Let us know more about your project!