Chatbots are configured to help users navigate sites and answer questions. They are a powerful ally for customer support and benefit companies and users alike, especially when they support several languages. Multilingual chatbots are transforming various sectors, from customer service and e-commerce to healthcare, banking, and tourism. Here is an overview of the evolution of this technology and how it’s impacting the customer support industry.

From Monolingual to Multilingual Communicators

The next step in chatbots’ evolution came with the advancements in machine translation. Neural machine translation (NMT) for instance, played a big role in equipping chatbots with multilingual capabilities in real-time, with better grammar, syntax, and overall language understanding than before. This was possible by learning from large datasets. By using vast amounts of multilingual data from conversations and translations, chatbots started to learn the differences between languages, and how to give better answers to prompts. On top of that, integrating NMT also increased scalability capabilities, since it allowed faster information processing, and for more target languages.

As with many other industries and technologies, the emergence of AI systems has taken the multilingual capabilities of chatbots to new heights. Nowadays, many chatbots use Large Language Models (LLMs) such as GPT, BERT, or others to process and produce written outputs, improving fluency considerably. Moreover, they have enabled multimodal processing capabilities giving room for a more comprehensive and intuitive communication experience. Now, 69% of customers prefer multilingual chatbots for real-time support and its usage seems to be only growing. It has proved to be a low-cost solution for customer support and client engagement in record time –in multiple languages.

Source: FastBots.ai

Overall Mechanics of Multilingual Chatbots

The way a chatbot is trained affects its capabilities. A chatbot can be:

• Scripted or ruled-based (keyword matching).
• Retrieval-based (using NLP and machine learning).
• LLM-based.

Ruled-based chatbots, for instance, are chatbots designed to answer scripted simple prompts. As such, they will have a limited use and lack any contextual understanding. Contrary to this, retrieval-based and AI-powered chatbots have a wider range of responses, applications, and the ability to learn from interaction. The difference between these two is the amount of data used to train the systems, which impacts language accuracy (source: Xue, Jintang, et al., 2023.)

Most current systems are AI-based or hybrid (which limits the responses to certain prompts). However, there are several technologies involved in chatbots’ functioning, including NLP, machine translation, deep learning, speech recognition, and LLMs. These technologies combined allow multilingual chatbots to:

1. Analyse users’ input to sort language preferences (text, speech, or images.)
2. Use machine translation and machine learning to process the information and give an answer to the user.
3. As chatbots interact with users, they employ machine learning techniques to continuously expand their linguistic knowledge.

Although this is an oversimplified overview, this adaptive learning process enables chatbots to provide increasingly accurate and natural-sounding responses over time. Enhancing these capabilities has allowed chatbots to reach more customers in record time. To give an idea of how much chatbots are taking over the customer service market: the market size value of the conversational AI market in 2022 was valued at USD 7,8 billion, and it’s projected to reach USD 48,81 billion in 2031.

Challenges and Ethical Considerations

Despite how far chatbots have come, there are still concerns regarding bias, privacy, and ethics, especially in the age of AI. Although its use has increased considerably and is integrated into most e-commerce and web service platforms, increasing revenue for companies; it is important to understand its limitations and flaws to avoid potential issues for your business and customers.

Data Privacy and Security

This is a common concern with AI technologies, especially after the advent of LLMs. Chatbots collect users’ information (personal and biometrical data sometimes) to assist them with different tasks. It is no wonder then, that one of the main concerns with this is whether the information shared with the bots will be used to train algorithms further or not.

Fortunately, as the use of LLMs spreads more and more, so do regulations and guidelines, and institutions hurry to keep up with measures to prevent leaks or re-use of personal data. It is important to put into place the right encryption protocols, and data storage practices while following regional privacy data protection laws.

Addressing Bias and Ensuring Fairness

Bias can affect the distribution of resources, decision-making, or perpetuation of harmful stereotypes. Studies have shown that in some cases, chatbots used in healthcare for diagnosing patients have resulted in putting African American patients at a disadvantage since they are suggested different diagnoses and treatments than other ethnicities with the same symptoms, thus affecting their access to medical specialists. Less visibly, but harmful nonetheless, bias in AI perpetuates harmful stereotypes that can affect the reputation, feelings, and overall perception of the world for –or about– different groups of people.

Bias comes from different sources, like design, interface, internal components (such as data and language models), or user interactions. Especially when it comes to data and language models, it is hard to control the information the chatbots are being trained with; as such, the answer the chatbot outputs might be culturally insensitive, offensive, or incorrect. To mitigate bias, developers are employing techniques such as debiasing algorithms, diverse data collection, and rigorous testing procedures with linguists, and cultural experts from diverse backgrounds.

Navigating Transparency and Accountability

Users should be aware of the chatbot’s capabilities, limitations, and the extent of its autonomy in decision-making. Transparency and accountability are essential when deploying multilingual chatbots, or any other technology that collects and uses information, particularly in sensitive domains like healthcare or finance.

These challenges are being addressed by implementing explainable AI techniques, which allow users to understand the reasoning behind the chatbot’s responses. Stakeholders are also establishing ethical review boards and developing industry-wide standards to ensure transparent and accountable development of multilingual chatbots.

Source: Xue, Jintang, et al., 2023

AI-powered virtual assistants are breaking down language barriers and delivering seamless, real-time assistance to customers across the globe. Gone are the days of frustrating language mismatches or the need to navigate a maze of phone menus. But the benefits go beyond just improved customer experience. Multilingual chatbots are also boosting operational efficiency, automatically handling a high volume of inquiries and freeing up human agents to tackle more complex issues. Customer support agents are not to disappear. This means faster response times, reduced costs, and the ability to scale support as needed –a game-changer for businesses looking to expand their global reach.

Word of caution: although we highlighted its benefits and how it’s changing the customer support industry, as with recent technologies, concerns about privacy, bias, and transparency must be addressed to prevent any harm to users. As usual, consider the pros and cons of technology and be aware of potential harm to minimize risks.