Localization using CLU multilingual capabilities
By integrating Conversational Language Understanding (CLU) with Teneo, you can benefit from the combined conversational power of both tools. One significant advantage of this approach is the ability to combine a Teneo bot with CLU multilingual capabilities in order to create a powerful multilingual bot based on a single CLU model. These already powerful capabilities of CLU are ideally combined with Teneo Linguistics Modeling Language (TLML) for control and accuracy.
This is what a conversation held in Spanish, English, Swedish, Turkish, and German with the final multilingual bot could look like:
- Spanish - Hola! (Hello)
- English - My name is John
- Swedish - Minns du vad jag heter? (Do you remember my name?)
- Turkish - İyi misin? (Are you well?)
- German - Auf wiedersehen (Goodbye)
On this page, we will cover the concepts behind this approach. For detailed steps on how to implement all of the following, check out our guide on the topic.
Prerequisites
These instructions assume you have access to a Teneo Sandbox as well as access to CLU.
Connect the CLU model to Teneo
Once you have access to a Teneo Sandbox and have created a CLU model it is time to set up the connection. The CLU model can be connected to the Teneo solution using the native integration from your sandbox.
Localize a bot
Once the CLU model has been trained and assigned to your Teneo solution, your Teneo solution will bypass Teneo's native classifier (Learn) and instead use the CLU native intent model. You can now localize bots to other languages and if you include all the same classes in your local solutions you can make use of the same CLU model you trained in the master solution.
To localize a bot, you must first include relevant content for branching, including flows, entities, variables, classes, and any other required solution content. If you forget to include something before branching, it can be added after the localized solution has been created.
After including content, you can create the localized solution via branching and translate flow components like names and outputs into the desired language. You can read more about Master and Local solutions here.