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.

翻译：本文描述了NOWJ1团队在2023年自动法律问答竞赛（ALQAC）中的技术方案，重点通过整合经典统计模型与预训练语言模型（PLMs）提升法律任务性能。针对文档检索任务，我们实现预处理步骤以突破输入限制，并应用学习排序方法整合多模型特征。问答任务被拆分为句子分类与答案抽取两个子任务：我们分别采用最先进模型为各子任务构建独立系统，同时运用经典统计模型与预训练语言模型。实验结果表明，我们所提出的方法在竞赛中展现出显著潜力。