Text generation model training method and device, storage medium and computer equipment

Abstract

The invention discloses a text generation model training method and device, a storage medium and computer equipment, and relates to the technical field of machine learning. The method comprises the following steps: acquiring question texts and a plurality of context texts in multiple rounds of dialogues; generating a first text vector matrix corresponding to the plurality of context texts and a reply text vector matrix corresponding to the question text by using an initial text vector generation model; utilizing an initial topic vector extraction model to respectively extract a context text topic vector corresponding to the first text vector matrix and a reply text topic vector corresponding to the reply text vector matrix; constructing a loss function based on the context text topic vector and the reply text topic vector; and jointly carrying out iterative training on the initial text vector generation model and the initial topic vector extraction model based on the loss function, and constructing a text generation model. The method is suitable for training the text generation model.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a training method, device, storage medium and computer equipment for a text generation model. Background technique [0002] In natural language processing, multi-round dialogue is always a research hotspot, and how to accurately and effectively generate reply text is of great significance to the research of multi-round dialogue. [0003] At present, it is usually based on the understanding of the context in multiple rounds of dialogue, using a deep learning model to generate the corresponding reply text. However, in the process of training the deep learning model, this method does not consider whether the generated reply text is deeply related to the contextual dialogue topics in the multi-round dialogue, which leads to the deviation of the reply text generated by the deep learning model from the multi-round dialogue. The overall logic cannot guarantee the accur...

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Problems solved by technology

However, in the process of training the deep learning model, this method does not consider whether the generated reply text is deeply related to the contextual dialogue topics in the multi-round dialogue, which leads to the deviation of the reply text generated by the deep learning model from the multi-round dialogue. The overall logic cannot guarantee the generation accuracy of the reply text

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