{"title":"应用基于bert的模型和领域知识在ALQAC 2021自动法律问答任务","authors":"Truong-Thinh Tieu, Chieu-Nguyen Chau, Nguyen-Minh-Hoang Bui, Truong-Son Nguyen, Le-Minh Nguyen","doi":"10.1109/KSE53942.2021.9648727","DOIUrl":null,"url":null,"abstract":"With robust development in NLP (Natural Language Processing) methods and Deep Learning, there are a variety of solutions to the problems in question answering systems that achieve extraordinary results. In this paper, we describe our approach using at the Automated Legal Question Answering Competition (ALQAC) 2021. In this competition, we achieved the first prize of all tasks with the scores of 88.07%, 71.02%, 69.89% in Task 1, Task 2 and Task 3 respectively.","PeriodicalId":130986,"journal":{"name":"2021 13th International Conference on Knowledge and Systems Engineering (KSE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Apply Bert-based models and Domain knowledge for Automated Legal Question Answering tasks at ALQAC 2021\",\"authors\":\"Truong-Thinh Tieu, Chieu-Nguyen Chau, Nguyen-Minh-Hoang Bui, Truong-Son Nguyen, Le-Minh Nguyen\",\"doi\":\"10.1109/KSE53942.2021.9648727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With robust development in NLP (Natural Language Processing) methods and Deep Learning, there are a variety of solutions to the problems in question answering systems that achieve extraordinary results. In this paper, we describe our approach using at the Automated Legal Question Answering Competition (ALQAC) 2021. In this competition, we achieved the first prize of all tasks with the scores of 88.07%, 71.02%, 69.89% in Task 1, Task 2 and Task 3 respectively.\",\"PeriodicalId\":130986,\"journal\":{\"name\":\"2021 13th International Conference on Knowledge and Systems Engineering (KSE)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"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/KSE53942.2021.9648727\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 13th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE53942.2021.9648727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Apply Bert-based models and Domain knowledge for Automated Legal Question Answering tasks at ALQAC 2021
With robust development in NLP (Natural Language Processing) methods and Deep Learning, there are a variety of solutions to the problems in question answering systems that achieve extraordinary results. In this paper, we describe our approach using at the Automated Legal Question Answering Competition (ALQAC) 2021. In this competition, we achieved the first prize of all tasks with the scores of 88.07%, 71.02%, 69.89% in Task 1, Task 2 and Task 3 respectively.
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