Meet My Multilingual Chatbot, George

welocalize March 29, 2021

George, by the way, is a chatbot. Far from being a novelty, chatbots have already become mainstream. They certainly have come a long way since Eliza. Eliza was the first chatbot created in 1966 at the Massachusetts Institute of Technology.

Chatbots are computer programs that interact with humans through text, messaging, or voice. With the advancement of artificial intelligence (AI) and its subsets machine learning (ML) and natural language processing (NLP), chatbots have become more conversational in tone. And they are increasingly becoming better at handling more complex interactions.

Chatbots all over the world have evolved and are now an important channel for customer interaction. According to Grand View Research, Inc., the global chatbot market is expected to reach US$1.25 billion by 2025. And a Statista survey showed that 67% of consumers worldwide interacted with a chatbot to get customer support.

With such international scope, companies should develop chatbots capable of conversing in multiple languages. Only 7.3% of the world’s population use English as their native language. According to CSA Research, most Internet users worldwide prefer to purchase products and chat with customer support in their own language.

Getting Started With a Multilingual Chatbot

An easy way for you to get started is to develop a rule-based chatbot. Let’s use George as an example. You set predefined rules and canned responses and then map out decision trees and flowcharts. Once integrated into your website, George can converse with customers.

While straightforward to set up, George is limited and inflexible. For instance, he can only answer simple questions and respond to queries that exactly match keywords defined in his database.

If you want George to be multilingual, you can build a separate rule-based bot for any language, such as French George or Chinese George. However, this is slow, inefficient, and costly.

Understanding Machine Learning Chatbot Technology

A better option is to create a machine learning chatbot, also called a conversational AI chatbot. If you build George as an ML chatbot, he can learn on his own from his conversations with users. As he is programmed using NLP, he understands the meaning behind sentences. As such, George the ML chatbot can better understand the intent and context of queries and generate relevant answers to complicated questions.

To make George chat in different languages simultaneously, you can plug in a machine translation API. You can offer a language menu for a user to choose from. Or you can program him to automatically detect the default language based on the IP address of the user.

Challenges of a Multilingual Machine Learning Chatbot

While using machine translation is fast and easy to implement, it’s not the most effective and accurate. If George relies solely on machine translation, he’ll simply translate word for word. And often, he could lose the context, nuances, and actual user intent.

It will also be challenging for him to understand mixed languages, regional dialects, and local slang. A straightforward dictionary translation can end up making George sound absurd, and for a customer, extremely frustrating to chat with.

That’s why George should still be supported and trained by a team of human translators, language designers, local linguists, and conversational architects. They can help George with localization, user intent, nonstandard language, and specialized terminology.

Benefits of Machine Learning Chatbots 

There are many ways a multilingual ML chatbot can help your global brand scale. With a chatbot like George, you can:

  • Improve customer service. George is available 24/7. He doesn’t need to sleep, eat, or go on holiday. Your customers can get instant answers anytime, anywhere. And he will patiently answer questions all day long.
  • Offer a convenient channel. Customers, especially those from younger generations, prefer chatting on messaging platforms than calling a contact center or sending an email. Your customers will love you for it.
  • Lower costs. Instead of hiring an army of human agents, George can handle inquiries and transactions simultaneously at a much lower cost. He can leave more complex and difficult issues to your human agents.
  • Increase revenues. Customers often need assistance while researching or purchasing online. George is there to answer questions like a salesperson, thus, increasing the possibility of closing sales.
  • Have different use cases. Beyond customer service, you can deploy chatbots for marketing, sales, tech support, and HR.
  • Humanize your brand. You give your company a face. You can give George a distinct personality aligned with your brand. Instead of dealing with a faceless company name, your customers get to chat with a human-like bot named George.

Working With a Team

To reap the full benefits of a multilingual ML chatbot, it’s best to work with a team that can help train your chatbot. Welocalize specializes in collecting, annotating, and evaluating the unstructured information of big data to create accurate, high-quality training data sets for machine learning.

Data transformation blends machine automation, human intelligence, and language understanding of over 525 language combinations. Welocalize works with a global network of 77,000 language experts. Contact us and we can help you create a multilingual chatbot that can respond to customers worldwide.