The present application relates to artificial intelligence and natural language processing, and provides a data enhancement model training method and apparatus. The method comprises: inputting first corpus data into a first seq2seq model to obtain second corpus data, and inputting the second corpus data into a second seq2seq model to obtain third corpus data; respectively inputting the first corpus data and the third corpus data into a text classification model for identification to obtain a classification result; calculating a loss value according to the classification result, and using the loss value to optimize parameters of the three models; performing loop iteration until a preset stopping condition is met. Training of a text classification model and a data enhancement model is a game process, the training purpose of the text classification model being to make a generated new corpus not easily distinguishable by the text classification model, and the training purpose of the data enhancement model being to accurately distinguish an original corpus or a generated new corpus. Thus, the data enhancement model can generate a corpus having a better effect, and really enhance and expand an original corpus.
DATA ENHANCEMENT MODEL TRAINING METHOD AND APPARATUS