Provided are a conversation intent recognition model training method, apparatus, device, and medium, relating to the technical field of semantic parsing. First conversation sample data is inputted into a search model constructed on the basis of elastic search (ES) to determine augmented sample data (S20); the first conversation sample data and the augmented sample data are inputted to an initial intent recognition model, and augmented intent recognition is performed on the first conversation sample data and the augmented sample data to obtain a first sample distribution and a second sample distribution (S30); distribution loss values are determined according to the first sample distribution and the second sample distribution, and a total loss value of the initial intent recognition model is determined according to the distribution loss values (S40); if the total loss value does not reach a preset convergence condition, then a first initial parameter of an initial intent recognition model is updated and iterated until the total loss value reaches the preset convergence condition, at which time the initial intention recognition model after convergence is recorded as a conversation intent recognition model (S50); thus the recognition accuracy of the intent recognition model is improved.
CONVERSATION INTENT RECOGNITION MODEL TRAINING METHOD, APPARATUS, COMPUTER DEVICE, AND MEDIUM