Unsupervised Legal Evidence Retrieval via Contrastive Learning with Approximate Aggregated Positive.

Verifying the facts alleged by prosecutors before the trial requires the judges to retrieve evidence within the massive materials accompanied. Existing Legal AI applications often assume the facts are already determined and fail to notice the difficulty of reconstructing them. To build practical Legal AI applications and free judges from the manual searching work, we introduce the task of Legal Evidence Retrieval, which aims to automatically retrieve precise fact-related verbal evidence within a single case. We formulate the task in a dense retrieval paradigm and jointly learn the contrastive representations and alignments between facts and evidence. To avoid tedious annotations, we construct an approximated positive vector for a given fact by aggregating a set of evidence from the same case. An entropy-based denoising technique is further applied to mitigate the impact of false positive samples. We train our models on tens of thousands of unlabeled cases and evaluate them on a labeled dataset containing 919 cases and 4, 336 queries. Experimental results indicate that our approach is effective and outperforms other state-of-the-art representation and retrieval models.