Localize with multilingual CLU
With Conversational Language Understanding (CLU), you can train a model in one language and use it to predict intents and entities from utterances in another language. This powerful feature can save a huge amount of time and effort; instead of building separate projects for every language, you can handle multi-lingual datasets in one project. Your dataset doesn't have to be entirely in the same language but you should enable the multi-lingual option for your project in the project settings.
Localizing a bot for different languages, markets, and/or cultures is a common use case. This is accomplished in Teneo through branching, where we have the option of creating a 'Master' version of a solution and its own 'Local' versions for each of the different languages we are interested in. In this way, Teneo allows us to create local solutions with all of the parts of the master solution that we want to include, making it an efficient way to create bots for different languages and markets. You can read more about localization here.
In this guide, we will demonstrate how to localize the 'Longberry Baristas' solution, which is in English, to other languages with the help of CLU. More information on branching can be found in Documentation.
By following the steps in this guide, you will complete the following:
- Train a multilingual CLU model on English from within Teneo.
- Create a localized Teneo solution in another language that uses the same CLU model.