A French Chatbot is a computer program that can engage into a conversation in a human-like manner. It can do so by analyzing and understanding French content to determine what is the best answer.
Chatbots can answer questions and help you accomplish a task. We’ve seen banks using chatbot to pre-qualify mortgage requests and the Quebec Government uses it to answer questions about Covid-19.
In recent years, the advances in natural language processing make it possible for a conversational assistant to not only understand the meaning of the words, but also the context based on the previous messages.
French is spoken by more than 250 million people on the planet. In fact, it’s the fifth most spoken language. It’s a language most people will find difficult to learn at first. Mostly because of the grammar exception, genders and accent marks.
However, it doesn’t make it more difficult for AI to process the language. As of today, there are models for hundreds of languages. It was once true that English was better understood by AI because most of the research was made in English, but this is no longer the case.
Most languages have regional differences and the French is no exception. If you are from France or Quebec, you have different expressions. In a supervised environment like Botpress, you can train your NLU to understand regional differences.
The first step for any natural language processing algorithm is making sense of the language i.e. parsing up the sentences into discrete units of meaning. This task is officially called the tokenizing of the language as each discrete unit of meaning is called a token.
The more systematic and orderly the language the easier it is to tokenize the language. As a general rule, if a language is difficult to learn for a human it will be difficult to learn for a computer program.
A long time ago in computer years, the job of tokenizing the language required a great deal of manual intervention on the part of the NLP researcher. Every language had to be tokenized independently and essentially manually.
Working with multiple languages can be difficult depending on the platform you use. Some platforms require chatbots with different languages to be built as separate chatbots which is obviously highly inefficient.
A good platform will be truly multi-lingual and will therefore allow multiple translations of all content within the user interface of the platform.
In addition, the language needs to be tracked as a variable of the conversation so that the AI can detect the language accurately and conversational designers can design logic around the language.
Aside from language specific functionality, to create a great chatbot the general functionality of the chatbot platform needs to be excellent. There are two categories of functionality that are important.
The quality of the conversational experience created for the end user is tightly coupled to the power of the tool you use to create it because the tool can become a limitation of your conversational design.
Even with a good platform, there are still challenges in creating a great chatbot in French. There are a limited number of French researchers in the AI world and therefore it can be challenging to get the right resources to work on the project. While it is not necessary to find resources to write the underlying NLU algorithms as these are provided out of the box, there can be a challenge in finding competent designers that can speak all the languages supported by the chatbot. It is therefore important that the chatbot platform allows the content and translations to be easily updated and maintained by non-technicals as it is likely that the designer does not speak all the supported languages.
Obviously the fact that high quality French chatbots are now coming online means that adoption of this technology will increase. This increasing adoption will solve the problems of resource constraints and allow potential buyers of the technology to get a clear idea of what the best practices are.
The breakthroughs in NLP technology apply not only to French chatbots but also to other AI applications. Often a chatbot is used as the user interface to not only different AI technologies but to help end users use screens of other systems, such as websites or web apps.
The next step for all NLU engines regardless of language is to do a better job at multi-turn dialogs. This means allowing a human to have a multi-step conversation with the chatbot in a narrow topic domain as opposed to just issuing one off commands or questions. And the related next step for the chatbot platforms is making it easy to create multi-turn dialogs.