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.
@article{arxiv.2309.09070,
title = {NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models},
author = {Tan-Minh Nguyen and Xuan-Hoa Nguyen and Ngoc-Duy Mai and Minh-Quan Hoang and Van-Huan Nguyen and Hoang-Viet Nguyen and Ha-Thanh Nguyen and Thi-Hai-Yen Vuong},
journal= {arXiv preprint arXiv:2309.09070},
year = {2023}
}