NOWJ1@ALQAC 2023：利用经典统计模型与预训练语言模型提升法律任务性能
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models
AI总结：
本文提出NOWJ1团队在ALQAC 2023中的方法，通过结合经典统计模型与预训练语言模型，并采用学习排序和子任务拆分策略，有效提升了法律文档检索与问答任务的性能。
摘要：
本文描述了NOWJ1团队参加2023年自动化法律问答竞赛（ALQAC）的方法，该竞赛的重点是通过整合经典统计模型和预训练语言模型（PLMs）来提升法律任务性能。在文档检索任务中，我们实现了一个预处理步骤以克服输入限制，并应用学习排序（learning-to-rank）方法来整合来自不同模型的特征。问答任务被分为两个子任务：句子分类和答案抽取。我们引入最先进的模型，为每个子任务开发不同的系统，同时利用经典统计模型和预训练语言模型。实验结果表明，我们提出的方法在竞赛中展现出有前景的潜力。
英文摘要：
This paper describes the NOWJ1 Team's approach for the Automated Legal Question Answering Competition (ALQAC) 2023, which focuses on enhancing legal task performance by integrating classical statistical models and Pre-trained Language Models (PLMs). For the document retrieval task, we implement a pre-processing step to overcome input limitations and apply learning-to-rank methods to consolidate features from various models. The question-answering task is split into two sub-tasks: sentence classification and answer extraction. We incorporate state-of-the-art models to develop distinct systems for each sub-task, utilizing both classic statistical models and pre-trained Language Models. Experimental results demonstrate the promising potential of our proposed methodology in the competition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
