nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models
This work addresses legal information extraction for legal professionals, but it is incremental as it applies existing methods to a competition without major innovations.
The paper tackled legal case retrieval and entailment tasks in the COLIEE-2022 competition by using a combination of neural models (Sentence-BERT and Sent2Vec) and traditional retrieval (BM25), resulting in a 5th-place ranking and showing that BM25 outperformed neural models.
This paper describes our submission to the Competition on Legal Information Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for tasks 1 and 2. Task 1 is a legal case retrieval task, which involves reading a new case and extracting supporting cases from the provided case law corpus to support the decision. Task 2 is the legal case entailment task, which involves the identification of a paragraph from existing cases that entails the decision in a relevant case. We employed the neural models Sentence-BERT and Sent2Vec for semantic understanding and the traditional retrieval model BM25 for exact matching in both tasks. As a result, our team ("nigam") ranked 5th among all the teams in Tasks 1 and 2. Experimental results indicate that the traditional retrieval model BM25 still outperforms neural network-based models.