Conversation Language Understanding Model (Part 1)

By Ashwin Venugopal -

 





Create, Train, and Deploy Conversation Language Understanding Model

Each customizable feature within Azure AI Language requires different procedures to create the models. The goal of conversational language understanding is to develop a model that can predict intentions based on conversational input. For instance, consider an email application that you can interact with through chat to send messages or flag items. You would train the model using phrases such as "please flag that email" or "I’m ready to send it." These phrases would be converted to intents for flag email and send email

To use language understanding you'll need to have an Azure AI Language resource already created in Azure, then you can carry out the following steps in Language Studio:

  • From the home page, you select Conversational Language Understanding in the Understand questions and conversational language tab.

  • Select + Create new project.

  • Enter a name for the new project.

  • Select your language.

  • Enter a description, then select Next.

  • Select Create.

Language Studio guides you through the remaining steps. Follow the left navigation from top to bottom to-

  • Create your schema definition. This involves adding all the intents and entities that your app is interested in.

  • Label data. You provide example chats and utterances along with how they map to entities and intents.

  • Train your model. Once you've added the data labeling information, you can start training your model. You can either split all your data with 80% for training and 20% for testing. Or you can create your own manual split.

  • Review the performance of your model.

  • Deploy your model. When you're happy with the performance of your model, you deploy it. This makes it available to be called as an API from your app and test it.

  • Test your deployment. This option allows you to test your model in the same way as preconfigured models

No matter which feature you utilize, you will ultimately have a model that can be implemented in applications to incorporate language comprehension.

Enrich Search Index in Azure AI Search With Custom Classes and Azure AI Language

You can map a text passage to various user-defined classes using custom text classification. To automatically determine a book's genre, for instance, you could train a model on the synopsis included on the back cover. You then add a genre component to your online store search engine using that defined genre.

You need to consider the following to enrich a search index using a custom text classification model-

  • Store your documents so they can be accessed by Language Studio and Azure AI Search indexers

  • Create a custom text classification project

  • Train and test your model

  • Create a search index based on your stored documents

  • Create a function app that uses your deployed trained model

  • Update your search solution, your index, indexer, and custom skillset

Conclusion

We have successfully learnt about creating, training, and deploying a conversation language understanding model.

















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