{"title":"Building a Chatbot for Supporting the Admission of Universities","authors":"Minh-Tien Nguyen, Manh Tran-Tien, Anh Phan Viet, Huy-The Vu, Van-Hau Nguyen","doi":"10.1109/KSE53942.2021.9648677","DOIUrl":null,"url":null,"abstract":"The admission process of universities in Vietnam is a labor-expensive task due to the involvement of humans. This paper introduces an intelligent system (a chatbot) that can support the admission process by automatically answering questions. Different from prior work that usually builds the bot from scratch, we develop the bot by using the Rasa platform. To do that, we investigate different combinations of components of natural language understanding to find the best pipeline. We also create and release a dataset in the admission domain to train the bot. Experimental results show that the pipeline using DIET with features from pre-trained language models is competitive. The introduction video of the system is also available.11https://youtu.be/gw7wvlADxnE","PeriodicalId":130986,"journal":{"name":"2021 13th International Conference on Knowledge and Systems Engineering (KSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 13th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE53942.2021.9648677","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The admission process of universities in Vietnam is a labor-expensive task due to the involvement of humans. This paper introduces an intelligent system (a chatbot) that can support the admission process by automatically answering questions. Different from prior work that usually builds the bot from scratch, we develop the bot by using the Rasa platform. To do that, we investigate different combinations of components of natural language understanding to find the best pipeline. We also create and release a dataset in the admission domain to train the bot. Experimental results show that the pipeline using DIET with features from pre-trained language models is competitive. The introduction video of the system is also available.11https://youtu.be/gw7wvlADxnE