Citation recommendation is the task of finding appropriate citations based on
a given piece of text. The proposed datasets for this task consist mainly of
several scientific fields, lacking some core ones, such as law. Furthermore,
citation recommendation is used within the legal domain to identify supporting
arguments, utilizing non-scholarly legal articles. In order to alleviate the
limitations of existing studies, we gather the first scholarly legal dataset
for the task of citation recommendation. Also, we conduct experiments with
state-of-the-art models and compare their performance on this dataset. The
study suggests that, while BM25 is a strong benchmark for the legal citation
recommendation task, the most effective method involves implementing a two-step
process that entails pre-fetching with BM25+, followed by re-ranking with
SciNCL, which enhances the performance of the baseline from 0.26 to 0.30
MAP@10. Moreover, fine-tuning leads to considerable performance increases in
pre-trained models, which shows the importance of including legal articles in
the training data of these models.

引用推荐是根据给定的文本寻找合适引用的任务，该研究通过收集第一个学术法律数据集，对现有模型进行了实验和性能比较。研究结果表明，对于法律引用推荐任务，BM25 是一个强大的基准方法，而最有效的方法是使用 BM25 + 进行预提取，然后使用 SciNCL 进行重新排序，这将基准方法的性能从 0.26 提升至 0.30 的 MAP@10。此外，对预训练模型进行微调可以显著提高性能，这说明在这些模型的训练数据中包含法律文章的重要性。

学术法律文章的引文推荐

Citation Recommendation on Scholarly Legal Articles

The effectiveness of the BERT model on multiple linguistic tasks has been
well documented. On the other hand, its potentials for narrow and specific
domains such as Legal, have not been fully explored. In this paper, we examine
how BERT can be used in the Arabic legal domain and try customizing this
language model for several downstream tasks using several different
domain-relevant training and testing datasets to train BERT from scratch. We
introduce the AraLegal-BERT, a bidirectional encoder Transformer-based model
that have been thoroughly tested and carefully optimized with the goal to
amplify the impact of NLP-driven solution concerning jurisprudence, legal
documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it
against three BERT variations for Arabic language in three natural languages
understanding (NLU) tasks. The results show that the base version of
AraLegal-BERT achieve better accuracy than the general and original BERT over
the Legal text.

本文介紹了 AraLegal-BERT 模型，評估其對於阿拉伯法律文件的表現。結果證明該模型在自然語言理解 (NLU) 任務上比一般的 BERT 及原始版本表現更為準確。