Research And Implementation Of Chat System Based On Background Knowledge

With the huge increase in the computing power of modern computers and the huge increase in the amount of data brought about by the popularization of the Internet,a new generation of artificial intelligence technology represented by deep learning is in the ascen-dant,and natural language processing technology has also been developed rapidly.As one of the most anticipated applications of natural language processing,the human-machine dialogue system has also become the focus of research.The human-machine dialogue system is a computer program that can use natural lan-guage to talk to people.It can be divided into task-oriented and non-task-oriented dialogue systems.The task-oriented dialogue system provides users with the functions of software systems such as ordering meals and setting alarm clocks.Non-task-oriented dialogue sys-tems mainly provide users with services such as small talk.This thesis mainly studies knowledge-based non-task generative dialogue system,which introduces knowledge into the end-to-end generative dialogue system to solve the problem of weak interactive abil-ity of the dialogue system.This thesis mainly improves the existing knowledge-driven dialogue model from the following two aspects:(1)A two-step data preprocessing model that refers to rewriting is proposed.In order to solve the problem of poor dialogue accuracy due to the weak semantic understanding of the dialogue system,this thesis studies the problem of ambiguity in language,and pro-poses a two-step referential rewriting model.The model divides the reference elimina-tion into a self-attention mechanism-based reference recognition step and a self-attention mechanism-based sentence rewriting step.The omitted pronouns in the model are found in the model,and the omitted words and questions are found.Synthetic rewriting of sen-tences.This thesis verifies the effectiveness of the two-step referential rewriting model through experiments.(2)A method of knowledge integration based on attention mechanism is proposed.In order to solve the problem of the diversity of dialogue response information,this thesis discusses the research on the integration of knowledge into the dialogue,and designs a knowledge integration module based on the attention mechanism.The module obtains relevant knowledge from the knowledge selector,and uses the context-dependent vector to perform attention calculation on the knowledge,so that it can filter the context-related knowledge and enhance the knowledge integration ability of the dialogue system.This thesis verifies the effectiveness of the knowledge fusion method based on the attention mechanism through experiments.Based on the above two improvements,this thesis proposes a generative dialogue model based on background knowledge.The model uses a context encoder and a knowl-edge encoder to respectively encode the dialogue context and its related knowledge,and uses a self-attention decoder to decode.The generative dialogue model based on back-ground knowledge can actually improve the dialogue system in terms of semantic under-standing and interactivity,which is proved in subsequent machine evaluation and manual evaluation experiments.At the same time,based on the above,the thesis designs and im-plements a chat robot platform based on work knowledge,which can interact with users effectively.