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Why Your Conversational AI Needs Good Utterance Data?
Utterance data, also known as training data, is crucial for a conversational AI system because it is used to teach the model how to understand and respond to user inputs. The quality and quantity of the utterance data can greatly impact the performance of the model. If the data is not diverse or representative enough, the model may not be able to understand or respond to a wide range of inputs or user intents. Additionally, if the data is not labeled or annotated correctly, the model may not be able to learn the correct associations between inputs and responses. Therefore, it is important to have high-quality, diverse, and representative utterance data in order to train a conversational AI system that can understand and respond to user inputs effectively.
Why Your Conversational AI Needs Good Utterance Data?
Utterance data, also known as training data, is crucial for a conversational AI system because it is used to teach the model how to understand and respond to user inputs. The quality and quantity of the utterance data can greatly impact the performance of the model. If the data is not diverse or representative enough, the model may not be able to understand or respond to a wide range of inputs or user intents. Additionally, if the data is not labeled or annotated correctly, the model may not be able to learn the correct associations between inputs and responses. Therefore, it is important to have high-quality, diverse, and representative utterance data in order to train a conversational AI system that can understand and respond to user inputs effectively.