NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic
  Statistical Models and Pre-trained Language Models
Tan-Minh Nguyen Xuan-Hoa Nguyen Ngoc-Duy Mai Minh-Quan Hoang Van-Huan Nguyen Hoang-Viet Nguyen Ha-Thanh Nguyen Thi-Hai-Yen Vuong
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.