Abstract: Techniques herein relate to improving quality of classification models for differentiating different user intents by improving the quality of training samples used to train the classification models. Pairs of user intents that are difficult to differentiate by classification models trained using the given training samples are identified based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with a first intent and a training sample associated with a second intent in the pair of intents are ranked based upon a similarity score between the two training samples in each pair of training samples. A particular pair of training samples with a highest similarity score is selected and provided as output with a suggestion for modifying the particular pair of training samples.

Abstract: Techniques for improving quality of classification models for differentiating different user intents by improving the quality of training samples used to train the classification models are described. Pairs of user intents that are difficult to differentiate by classification models trained using the given training samples are identified based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with a first intent and a training sample associated with a second intent in the pair of intents are ranked based upon a similarity score between the two training samples in each pair of training samples. The identified pairs of intents and the pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options or suggestions for improving the training samples.