Man-machine dialogue method, device, storage medium and computer program product

Abstract

This application proposes a man-machine dialogue method, device, storage medium, and computer program product, wherein the method includes: determining the current dialogue topic and the user's current utterance information; determining the user's current utterance representation vector according to the current utterance information; combining the current utterance Information and the current discourse representation vector, perform graph reasoning calculation on the heterogeneous knowledge graph corresponding to the current dialogue topic, select the current knowledge corresponding to the current discourse information from the heterogeneous knowledge graph; according to the current discourse information and current knowledge, generate the current sentence corresponding to Reply sentences, among them, the heterogeneous knowledge graph is created based on structured knowledge and unstructured knowledge, and can generate rich reply sentences. In addition, the use of graph reasoning algorithms can improve the accuracy of knowledge selection, making the knowledge selection process It has good interpretability and good generalization ability, and at the same time, reduces the dependence of the overall solution on labeled corpus.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular, to a method, device, storage medium and computer program product for human-computer dialogue. Background technique [0002] In the current dialogue system, for the dialogue topic, the corresponding background knowledge includes structured knowledge and unstructured knowledge, while the dialogue system only considers one of them, and vectorizes each knowledge in one of them, and obtains each knowledge. Knowledge representation of knowledge; obtain the discourse representation corresponding to the user's current discourse and the above information; according to the knowledge representation and discourse representation of each knowledge, combined with neural network models, etc., select some knowledge from each knowledge to generate the reply sentence corresponding to the current discourse . [0003] However, in the above scheme, if only structured knowledge is consid...

AI Technical Summary

Problems solved by technology

[0003] However, in the above scheme, if only structured knowledge is considered, because structured knowledge is generally an entity or a word, etc., it is impossible to generate rich reply sentences; if only unstructured sentences are considered, since unstructured sentences are generally sentences, Lack of structured organization, it is difficult to select appropriate knowledge, and the reply sentences generated based on multiple sentences are likely to be confused or inconsistent with the actual facts

In addition, the above scheme has the problems of low accuracy of knowledge selection and poor interpretability

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