How to annotate conversational text for chatbot use cases

As AI continues to grow, there's been a rapid adoption in the industrial application of language-based models. Teams interested in training chatbots or other language models can now use Labelbox's Conversational Text editor.

Our Conversational Text editor allows teams to create unique message-based classifications that can identify user intent or sentiment. Teams can easily train an open-source model on their own data and use Labelbox's suite of tools across Annotate, Catalog, and Model to quickly tailor their language model to meet their specific business needs.

With our Conversational Text editor, teams can:

Natively import conversational text or thread-based messages
Create entities for NER
Create message-based classifications to identify user intent or sentiment
Create radio, checklist, or free-form text classifications
Create annotation relationships between entities
Use hotkeys to speed up labeling

To learn more about our Conversational Text editor, refer to our documentation.

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