Intents, Utterances, and Entities (Part 2)

By Ashwin Venugopal -

 




Use Patterns To Differentiate Similar Utterances

A model may occasionally have several intents for which utterances are expected to be similar. While reducing the quantity of sample utterances, you can leverage the pattern of utterances to clarify the intents. Additionally, the intents could apply to a wide range of entity values. To correctly train your model, provide a handful of examples of each intent that specify the different formats of utterances.

TurnOnDevice:

"Turn on the {DeviceName}"

"Switch on the {DeviceName}"

"Turn the {DeviceName} on"

GetDeviceStatus:

"Is the {DeviceName} on[?]"

TurnOffDevice:

"Turn the {DeviceName} off"

"Switch off the {DeviceName}"

"Turn off the {DeviceName}"

When you teach your model with each different type of utterance, the Azure AI Language service can learn how to categorize intents correctly based off format and punctuation.

Use Pre-Built Entity Components

To recognize common items like numbers, emails, URLs, or options, you can frequently utilize prebuilt components, but you can also build your own language models by describing all the intents and utterances it needs.

By using prebuilt components, you can eliminate the need to train your model with examples of the specified type of entity by allowing the Azure AI Language service to identify it automatically.

You can build an entity in your project and then choose Add new prebuilt to that entity to identify specific entities in order to add a prebuilt component. You can have up to five prebuilt components per entity. Using prebuilt model elements can significantly reduce the time it takes to develop a conversational language understanding solution.

Train, Test, Publish, and Review a Conversational Language Understanding Model

Creating a model is an iterative process with the following activities:

  • Train a model to learn intents and entities from sample utterances.

  • Test the model interactively or using a testing dataset with known labels

  • Deploy a trained model to a public endpoint so client apps can use it

  • Review predictions and iterate on utterances to train your model

By following this iterative approach, you can improve the language model over time based on user input, helping you develop solutions that reflect the way users indicate their intents using natural language.

Conclusion

We have successfully learnt about intent, utterances, and entities.












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