{"title":"Entertainment robots based on digital new media application in real-time error correction mode for Chinese English translation","authors":"Yanmei Geng","doi":"10.1016/j.entcom.2024.100789","DOIUrl":null,"url":null,"abstract":"<div><p>With the assistance of digital new media technology, virtual entertainment robots, as a new learning experience mode, can effectively enhance the interactive process of e-learning learning. This article studies the application of entertainment robots based on digital new media in real-time error correction mode for Chinese English translation. Through experiments, it has been verified that the flexible use of deep learning technology can significantly improve user satisfaction and translation accuracy, and has already improved the level of error correction and positioning. This article first introduces the existing mainstream machine learning models, including supervised neural network models and attention mechanisms. On this basis, the system was optimized to further improve its performance. At the same time, this article proposes new improvement plans to address the shortcomings of current mainstream translation systems. We conducted comparative experiments on the error correction model of the proposed adaptive algorithm for specific error types, and also tested it using real datasets. Research has shown that using adaptive algorithms based on reinforcement deep learning can not only significantly optimize the error correction efficiency of our system, but also flexibly adapt to the needs of various optimization strategies.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Entertainment Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1875952124001575","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
With the assistance of digital new media technology, virtual entertainment robots, as a new learning experience mode, can effectively enhance the interactive process of e-learning learning. This article studies the application of entertainment robots based on digital new media in real-time error correction mode for Chinese English translation. Through experiments, it has been verified that the flexible use of deep learning technology can significantly improve user satisfaction and translation accuracy, and has already improved the level of error correction and positioning. This article first introduces the existing mainstream machine learning models, including supervised neural network models and attention mechanisms. On this basis, the system was optimized to further improve its performance. At the same time, this article proposes new improvement plans to address the shortcomings of current mainstream translation systems. We conducted comparative experiments on the error correction model of the proposed adaptive algorithm for specific error types, and also tested it using real datasets. Research has shown that using adaptive algorithms based on reinforcement deep learning can not only significantly optimize the error correction efficiency of our system, but also flexibly adapt to the needs of various optimization strategies.
期刊介绍:
Entertainment Computing publishes original, peer-reviewed research articles and serves as a forum for stimulating and disseminating innovative research ideas, emerging technologies, empirical investigations, state-of-the-art methods and tools in all aspects of digital entertainment, new media, entertainment computing, gaming, robotics, toys and applications among researchers, engineers, social scientists, artists and practitioners. Theoretical, technical, empirical, survey articles and case studies are all appropriate to the journal.