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- W4366999416 abstract "Legal case retrieval, which aims to find relevant cases for a query case, plays a core role in the intelligent legal system. Despite the success that pre-training has achieved in ad-hoc retrieval tasks, effective pre-training strategies for legal case retrieval remain to be explored. Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. However, most existing language models have difficulty understanding the long-distance dependencies between different structures. Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements. Even subtle differences in key legal elements can significantly affect the judgement of relevance. However, existing pre-trained language models designed for general purposes have not been equipped to handle legal elements." @default.
- W4366999416 created "2023-04-27" @default.
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- W4366999416 date "2023-07-18" @default.
- W4366999416 modified "2023-09-26" @default.
- W4366999416 title "SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval" @default.
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- W4366999416 doi "https://doi.org/10.1145/3539618.3591761" @default.
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