AimeLaw at ALQAC 2021: Enriching Neural Network Models with Legal-Domain Knowledge

Huy Ngo, Tuan Nguyen, Duong Nguyen, Minh Pham

Abstract: In this paper, we present our participated systems for three Vietnamese legal text processing tasks at Automated Legal Question Answering Competition (ALQAC 2021). In our systems, we leverage the strength of traditional information retrieval methods (BM25), pre-trained masked language models (BERT), and legal domain knowledge. Our proposed methods help to overcome the shortage of training data. Especially, in the legal textual entailment task, we propose a novel data augmentation method that is based on legal domain knowledge. Evaluation results show the effectiveness of our proposed methods. Our team (AimeLaw) obtained the first prize in Task 2 (legal textual entailment) with 69.89% of accuracy; ranked second in Task 1 (legal document retrieval) with 80.61% of F2 and in Task 3 (legal question answering) with 64.77% of accuracy. We even improved the result on Task 2 to 72.16% in an extra experiment.