Apply Bert-based models and Domain knowledge for Automated Legal Question Answering tasks at ALQAC 2021

Truong-Thinh Tieu, Chieu-Nguyen Chau, Nguyen-Minh-Hoang Bui, Truong-Son Nguyen, Le-Minh Nguyen
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引用次数: 6

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.
应用基于bert的模型和领域知识在ALQAC 2021自动法律问答任务
随着NLP(自然语言处理)方法和深度学习的蓬勃发展,问答系统中出现了各种各样的问题解决方案,并取得了非凡的成果。在本文中,我们描述了我们在2021年自动法律问答竞赛(ALQAC)中使用的方法。在本次比赛中,我们在任务1、任务2和任务3中分别以88.07%、71.02%、69.89%的成绩获得了所有任务的第一名。
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