5 principles for good natural language understanding (NLU) design
Blog post from Voiceflow
The text discusses various techniques for improving the design and management of Natural Language Understanding (NLU) models, particularly in conversational AI applications. It highlights the importance of minimizing utterance overlap, achieving intent balance, using real-world data, setting confidence thresholds, and looking for other patterns to improve NLU accuracy and effectiveness. The text also emphasizes the need for iterative UX processes to refine both NLU models and conversational designs, and encourages readers to test their conversational assistants with diverse datasets that reflect the context of deployment. By applying these techniques, developers can create more effective language models that provide better customer experiences.
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