{"title":"基于强化学习的生成对话系统研究","authors":"永 颜","doi":"10.12677/hjdm.2023.132018","DOIUrl":null,"url":null,"abstract":"An open dialogue system model with diverse responses is constructed to try to solve the mono-tonous questions answered by the dialogue system during the response process. This paper proposes a generative dialogue method that combines bidirectional short-term memory neural network and reinforcement learning model. First, the corpus is preprocessed with various types of filters, so that the discourse corpus can be explored in a variety of ways; Secondly, in order to in-*","PeriodicalId":57348,"journal":{"name":"数据挖掘","volume":"37 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Generative Dialogue System Based on Reinforcement Learning\",\"authors\":\"永 颜\",\"doi\":\"10.12677/hjdm.2023.132018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An open dialogue system model with diverse responses is constructed to try to solve the mono-tonous questions answered by the dialogue system during the response process. This paper proposes a generative dialogue method that combines bidirectional short-term memory neural network and reinforcement learning model. First, the corpus is preprocessed with various types of filters, so that the discourse corpus can be explored in a variety of ways; Secondly, in order to in-*\",\"PeriodicalId\":57348,\"journal\":{\"name\":\"数据挖掘\",\"volume\":\"37 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"数据挖掘\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.12677/hjdm.2023.132018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"数据挖掘","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.12677/hjdm.2023.132018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Generative Dialogue System Based on Reinforcement Learning
An open dialogue system model with diverse responses is constructed to try to solve the mono-tonous questions answered by the dialogue system during the response process. This paper proposes a generative dialogue method that combines bidirectional short-term memory neural network and reinforcement learning model. First, the corpus is preprocessed with various types of filters, so that the discourse corpus can be explored in a variety of ways; Secondly, in order to in-*