Model training methods and apparatuses, text processing methods and apparatuses, and computer devices

Abstract

Computer-implemented methods, apparatuses, and computer-readable media for model training and text processing are disclosed. In an example, an original text, a positive sample, and a negative sample are obtained. The positive sample and the negative sample are rewritten texts of the original text. A first, second, and third distribution parameter corresponding to the original text, the positive sample, and the negative sample, respectively, are determined by using an encoder. The first, second, and third distribution parameter are used to describe a first, second, and third semantic distribution, respectively. A model parameter of the encoder is optimized by using a loss function based on the first, second, and third distribution parameter. The loss function includes a first term that is used to constrain a degree of similarity between the first and second semantic distribution to be higher than a degree of similarity between the first and the third semantic distribution.