  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.
