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Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
[article]
2022
arXiv
pre-print
As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications – the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match,
arXiv:2207.04182v1
fatcat:2oel57qo3bfrvhy2cixmjslpta