Series: Four perspectives you shouldn’t miss when selecting a Conversational AI platform – #2 language support and localization
As part of a series, Daniel Eriksson, Chief Innovation and Customer Success Officer at Artificial Solutions, gives insight on important aspects of a conversational AI platform that buyers often overlook. In this second post, he will focus on the importance of language support and localization when choosing the ideal Conversational AI platform.
Language Support & Localization
What is “language support”?
A conversational bot’s output – what the bot is saying – is almost always written by the implementor. It is like a manuscript written by your team that the bot follows. There can be variations of output generated by the bot that allows for a little more natural conversation, but most clients have a bot for a specific domain or purpose, so the degrees of freedom are fairly small. What the bot is saying to the users is largely designed by the bot development team. Therefore, the bot’s output is very controlled. So, in terms of output – language support does not mean that much – it can simply be that the specific language characters do not become garbled in the output. To help with the output, a bot development tool might have libraries available for some generic output like social talk and standard responses.
In short – conversational language support has very little to do with what the bot is “saying to” your users.
Language support is almost entirely about how the bot can support and map INPUT for a specific language. Can the bot accept Cyrillic characters without spitting out an error message? Can the bot split a sentence in Japanese to identify the verbs, nouns and adjectives? Can the bot, without training, classify words or phrases in a Spanish sentence?
What is important for a bot developer is that the language supports help them build bots for a specific language. So can the bot platform for a specific language “accept an input and annotate (add metadata)” so that the developer can make use of this to design a good conversational bot experience.
Different conversational AI platforms might claim to support German. But for one platform it can mean “the bot accept German characters” and for the other “the bot has an extensive annotation of words, phrases, concepts, entities, and pre-built knowledge in German”. Always try to read behind the simple description of a products language support. And think about what languages your company need to deploy bots in.
What is “localization” and how is it valuable?
Most companies today want to roll out successful conversational bot implementation in more than one language. This happens from different reasons. Sometimes businesses operate in countries or regions like Switzerland with several official languages, or they want to expand their market and deploy a bot in a neighbouring country. You can even say that deploying the same bot in two different channels like phone and Facebook messenger might require two different “languages” or at least tonalities.
Now here comes the tricky part – how do you develop and maintain a bot which is MOSTLY the same but in two or more languages? Do you just copy it and try to maintain two or more parallel solutions? Anyone who has tried this knows that this is a recipe for failure, or at least very inefficient and resource demanding way of working.
One solution to this problem is called “localization”.
Localization means you can create a master solution and then localize it for each specific language/region. The localized solution is linked to the master solution so that updates and changes can be propagated (under change control) to the local solutions.
Localization is not only about languages. If the same conversational bot flow needs to support two different back-end systems for two different regions – then localization should support that feature as well. You need to be able to make variations to the local implementation, without losing the benefit of the link to the master solution.
For most companies with an international outlook, consider the benefits of requiring your conversational AI development platform to support localization. Think about what languages support and variations you need long term, and make sure the toolset you chose gives your room to grow.