NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models

• URL: http://arxiv.org/abs/2309.09070v1
• Date: Sat, 16 Sep 2023 18:32:15 GMT
• Title: NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models
• Authors: 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
• Abstract summary: This paper describes the NOWJ1 Team's approach for the Automated Legal Question Answering Competition (ALQAC) 2023. 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. 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.
• Score: 4.329463429688995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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