BERT-LBIA: A BERT-Based Late Bidirectional Interaction Attention Model for Legal Case Retrieval

Most legal case retrieval methods rely on pre-trained language models like BERT, which can be slow and inaccurate. Alternatively, representation-based models provide quick responses but may not be the most accurate. To address these issues, our paper proposes a BERT-based late bidirectional interaction attention model for similar legal case retrieval. We use a dual BERT model as our backbone network to obtain feature representations of a query and its case candidates. Then, we develop a bidirectional interaction attention network to generate deep interactive attention signals between the query and its corresponding case candidates. Our experiments show that our model is faster and more accurate than existing retrieval models.