计算机科学 ›› 2025, Vol. 52 ›› Issue (2): 222-230.doi: 10.11896/jsjkx.240600081
刘衍伦, 肖正, 聂振宇, 乐雨泉, 李肯立
LIU Yanlun, XIAO Zheng, NIE Zhenyu, LE Yuquan, LI Kenli
摘要： 研究人员曾致力于通过案件匹配的方法找到相似的案件,但案件匹配的方法依赖于文本相似性,文本相似并不等同于案件相似;而且案件匹配的方法普遍缺乏解释性。为了克服案件匹配的缺点,定义了一个新问题,即案情要素关联与证据提取。该问题旨在基于案情要素而非文本相似性来预测关联结果,并提取关键事实细节作为证据以解释关联结果,这一新问题更符合法律从业者的实际需求。为了使所提出的模型在这一新问题上表现更好,引入了对比学习,以解决模型在获取文本表征时过度依赖案情要素直接表达的问题,从而使注意力权重均衡分布在相同案情要素的不同表达上,进而提升模型效果。在公开数据集和自建数据集上进行了实验。实验结果表明,与文本匹配模型相比,所提模型在accuracy和precision上均提高了约20%,在recall和F1上均提高了约30%。
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| [1]WANG J L,WU Y L.Research on the application of similar case retrieval system in judicial practice[J].Legality Vision,2022(2):100-102. [2]YU W,SUN Z,XU J,et al.Explainable legal case matching via inverse optimal transport-based rationale extraction[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.2022:657-668. [3]LIMSOPATHAM N.Effectively leveraging BERT for legal do-cument classification[C]//Proceedings of the Natural Legal Language Processing Workshop 2021.2021:210-216. [4]CHALKIDIS I,FERGADIOTIS M,MALAKASIOTIS P,et al.LEGAL-BERT:The Muppets straight out of Law School[C]//Findings of the Association for Computational Linguistics:EMNLP 2020.2020:2898-2904. [5]ZHENG L,GUHA N,ANDERSON B R,et al.When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings[C]//Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law.2021:159-168. [6]PAUL S,MANDAL A,GOYAL P,et al.Pre-trained languagemodels for the legal domain:a case study on Indian law[C]//Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law.2023:187-196. [7]KRASADAKIS P,SAKKOPOULOS E,VERYKIOS V S.Asurvey on challenges and advances in natural language processing with a focus on legal informatics and low-resource languages[J].Electronics,2024,13(3):648. [8]LI H,AI Q,CHEN J,et al.SAILER:structure-aware pre-trained language model for legal case retrieval[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.2023:1035-1044. [9]SONG D,GAO S,HE B,et al.On the effectiveness of pre-trained language models for legal natural language processing:An empirical study[J].arXiv.2204.03498,2022. [10]CHALKIDIS I,GARNEAU N,GOANTA C,et al.LeXFiles and LegalLAMA:Facilitating English Multinational Legal Language Model Development[C]//The 61st Annual Meeting of the Association for Computational Linguistics.2023. [11]PENG D,YANG J,LU J.Similar case matching with explicitknowledge-enhanced text representation[J].Applied Soft Computing,2020,95:106514. [12]FANG J,LI X,LIU Y.Low-Resource Similar Case Matching in Legal Domain[C]//International Conference on Artificial Neural Networks.Cham:Springer Nature Switzerland,2022:570-582. [13]BHATTACHARYA P,GHOSH K,PAL A,et al.Legal casedocument similarity:You need both network and text [J].Information Processing & Management,2022,59(6):103069. [14]BIBAL A,LOGNOUL M,DE STREEL A,et al.Legal requirements on explainability in machine learning[J].Artificial Intelligence and Law,2021,29:149-169. [15]ZHONG H,WANG Y,TU C,et al.Iteratively questioning and answering for interpretable legal judgment prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:1250-1257. [16]SHU Y,ZHAO Y,ZENG X,et al.Cail2019-fe:Tech.Rep.[R].2019. [17]LIU H S,WANG L,SUN Y Y,et al.Case element recognition method based on pre-trained language model[J] Journal of Chinese Information Processing,2021,35(11):91-100. [18]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).2019:4171-4186. [19]SHEN Z,LIU Z,LIU Z,et al.Un-mix:Rethinking image mixtures for unsupervised visual representation learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:2216-2224. [20]WANG T,ISOLA P.Understanding contrastive representation learning through alignment and uniformity on the hypersphere[C]//International conference on machine learning.PMLR,2020:9929-9939. [21]YAN Y,LI R,WANG S,et al.A contrastive framework for self-supervised sentence representation transfer[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.2021. [22]GAO T,YAO X,CHEN D.SimCSE:Simple Contrastive Lear-ning of Sentence Embeddings[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Proces-sing.Association for Computational Linguistics,2021. [23]NGUYEN H T,PHI M K,NGO X B,et al.Attentive deep neural networks for legal document retrieval[J].Artificial Intelligence and Law,2024,32(1):57-86. [24]XIAO C,HU X,LIU Z,et al.Lawformer:A pre-trained lan-guage model for chinese legal long documents[J].AI Open,2021,2:79-84. [25]SHAO Y,MAO J,LIU Y,et al.BERT-PLI:Modeling para-graph-level interactions for legal case retrieval[C]//IJCAI.2020:3501-3507. [26]CAO F X,SUN Y Y,WANG Z Z,et al.Similar case matching model for loan cases[J].Computer Engineering,2024,50(1):306-312. [27]CHEN C,LI K,TEO S G,et al.Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks[J].ACM Transactions on Knowledge Discovery from Data(TKDD),2020,14(4):1-23. [28]LIU C,LI K,LI K,et al.A new service mechanism for profit optimizations of a cloud provider and its users[J].IEEE Transactions on Cloud Computing,2017,9(1):14-26. [29]TALEB T,KSENTINI A,FRANGOUDIS P A.Follow-mecloud:When cloud services follow mobile users[J].IEEE Transactions on Cloud Computing,2016,7(2):369-382. [30]SHAO Y,WU Y,LIU Y,et al.Investigating user behavior in legal case retrieval[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:962-972. [31]LIU B,WU Y,LIU Y,et al.Conversational vs traditional:Comparing search behavior and outcome in legal case retrieval[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:1622-1626. [32]MINOCHA A,SINGH N,SRIVASTAV A A.Finding relevant indian judgments using dispersion of citation network[C]//Proceedings of the 24th international conference on World Wide Web.2015:1085-1088. [33]TIAN Y,SUN C,POOLE B,et al.What makes for good views for contrastivelearning?[J].Advances in Neural Information Processing Systems,2020,33:6827-6839. [34]GUO J,FAN Y,JI X,et al.Matchzoo:A learning,practicing,and developing system for neural text matching[C]//Procee-dings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:1297-1300. [35]HU B,LU Z,LI H,et al.Convolutional neural network architectures for matching natural language sentences[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2.2014:2042-2050. [36]TALMAN A,YLI-JYRÄ A,TIEDEMANN J.Sentence embeddings in NLI with iterative refinement encoders[J].Natural Language Engineering,2019,25(4):467-482. [37]WAN S,LAN Y,GUO J,et al.A deep architecture for semantic matching with multiple positional sentence representations[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2016. [38]WAN S,LAN Y,XU J,et al.Match-SRNN:modeling the recursive matching structure with spatial RNN[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:2922-2928. [39]WANG Z,HAMZA W,FLORIAN R.Bilateral multi-perspective matching for natural language sentences[C]//International Joint Conference on Artificial Intelligence.2017. [40]CHEN Q,ZHU X,LING Z H,et al.Enhanced LSTM for Natural Language Inference[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Vo-lume 1:Long Papers).Association for Computational Linguistics,2017. [41]DAI Z,XIONG C,CALLAN J,et al.Convolutional neural networks for soft-matching n-grams in ad-hoc search[C]//Procee-dings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:126-134. [42]HUANG P S,HE X,GAO J,et al.Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM international conference on Information & Knowledge Management.2013:2333-2338. [43]PARANJAPE B,JOSHI M,THICKSTUN J,et al.An Information Bottleneck Approach for Controlling Conciseness in Ratio-nale Extraction[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).Association for Computational Linguistics,2020. [44]JAIN S,WIEGREFFE S,PINTER Y,et al.Learning to faithfully rationalize by construction[C]//58th Annual Meeting of the Association for Computational Linguistics(ACL 2020).2020:4459-4473. [45]PRUTHI D,DHINGRA B,NEUBIG G,et al.Weakly-and Semi-supervised Evidence Extraction[C]//Findings of the Association for Computational Linguistics:EMNLP 2020.2020:3965-3970. [46]LI D,HU B,CHEN Q,et al.Unifying model explainability and robustness for joint text classification and rationale extraction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:10947-10955. |
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