{"title":"Long short-term memory networks for automatic generation of conversations","authors":"T. Fujita, Wenjun Bai, Changqin Quan","doi":"10.1109/SNPD.2017.8022766","DOIUrl":null,"url":null,"abstract":"Human Machine Interface demands the communicative propriety that would be applied in various linguistic tasks. In this research, we develop an intelligent ‘chat bot’, which generates conversational sentences via recurrent neural network and its coupled memory unit, long short-term memory (LSTM). Word strings in conversations are considered as time series data. Using a single neural network model that performs a simple task of outputting the next word from the preceding word, a conversational sentence can be generated by connecting the words. In the experiment, we performed the linguistic ‘Turning Test’ to evaluate the proposed system.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2017.8022766","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Human Machine Interface demands the communicative propriety that would be applied in various linguistic tasks. In this research, we develop an intelligent ‘chat bot’, which generates conversational sentences via recurrent neural network and its coupled memory unit, long short-term memory (LSTM). Word strings in conversations are considered as time series data. Using a single neural network model that performs a simple task of outputting the next word from the preceding word, a conversational sentence can be generated by connecting the words. In the experiment, we performed the linguistic ‘Turning Test’ to evaluate the proposed system.