Research And Application On Dialogue Generation Based On Knowledge Perception And Reasoning

The human-machine dialogue system is a technology that has attracted much attention from academia to industry in the field of natural language processing.It is also a challenging task towards general artificial intelligence.According to application scenarios,the human-machine dialogue system can be mainly divided into chit-chat based open-domain dialogue system and service-based task-oriented dialogue system.With the rapid development of deep neural networks,end-to-end generative dialogue models have become a research hotspot in recent years due to their effectiveness and ease of deployment.However,existing open-domain dialogue systems still suffer from the incorporation of commonsense knowledge and cannot generate high-quality responses by combining with background knowledge.Task-oriented dialogue systems often require multiple rounds of interaction with users to complete specific tasks.As the dialogue context becomes longer,the reasoning ability of dialogue models towards the knowledge in the domain-specific knowledge base becomes poorer,which makes it difficult to respond to the user's goals accurately.Therefore,this thesis aims to study knowledge perception and reasoning in end-to-end generative dialogue systems.For the shortcomings of existing dialog generation models,we explore generative dialogues from the two aspects,knowledge perception and knowledge reasoning.Furthermore,we propose effective models and algorithms.For the commonsense knowledge incorporation in open-domain dialogue systems,this thesis proposes an open-domain dialogue generation model based on knowledge perception,which transfers the ability of question representation and knowledge matching from the Knowledge Base Question Answering(KBQA)to assist dialogue generation.It helps the dialogue generation model understand the commonsense knowledge in the input utterance and generate knowledge entities related to the facts.In addition,this thesis proposes a response guiding attention mechanism and a multi-step decoding strategy,which help the dialogue model better capture relevant features for response generation.For the knowledge reasoning in task-oriented dialogue systems,this thesis proposes a Dual Dynamic Memory Network(DDMN)model,which includes two core modules: dialogue memory manager and knowledge base memory manager.The dialogue memory manager tracks long-term multiple turns of dialogue state information,and effectively captures the information in the current turn through a dynamic updating mechanism,while the knowledge base memory manager effectively acquires domain-specific knowledge through a dynamic memory pointer.The separation and interaction between the two modules make the dialogue decoder perform knowledge reasoning better and generate high-quality responses.In this thesis,comparative experiments are conducted on multiple open dialogue datasets.The experimental results demonstrate that the methods proposed in this thesis achieve significantly better results than the baseline models on both automatic evaluation with multiple metrics and human evaluation.This thesis also explores the superiority and shortcomings of our methods through model ablation analysis,error analysis,and display of generation results.