Legal Intelligence法律智能研究旨在赋予机器理解法律文本的能力。近些年来,随着以裁判文书为代表的司法大数据不断公开,以及自然语言处理技术的不断突破,如何将人工智能技术应用在司法领域,来提高司法人员在案件处理环节的效率逐渐成为法律智能研究的热点。我国的智慧法院、智慧检务建设等国家重大工程,就是法律人工智能的落地应用。目前,研究者们的注意力主要集中在法律要素抽取、自动判决、类案检索等任务上。
Zhong Haoxi, Xiao Chaojun,Tu Cunchao, Zhang Tianyang,Liu Zhiyuan,Sun Maosong
ACL, pp.5218-5230, (2020)
Legal Judgment Prediction and Similar Case Matching can be regarded as the core function of judgment in Civil Law and Common Law system, while Legal Question Answering can provide consultancy for those who are unfamiliar with the legal domain
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Haoxi Zhong, Chaojun Xiao,Cunchao Tu, Tianyang Zhang,Zhiyuan Liu,Maosong Sun
national conference on artificial intelligence, (2020)
We present JEC-QA as a new and challenging dataset for Legal Question Answering, and JEC-QA is the largest dataset in LQA
Cited by7BibtexViews267
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Xu Nuo,Wang Pinghui, Chen Long, Pan Li, Wang Xiaoyan,Zhao Junzhou
ACL, pp.3086-3095, (2020)
We present an end-to-end model, Law Article Distillation based Attention Network, to solve the issue of confusing charges in Legal judgment prediction
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empirical methods in natural language processing, pp.763-780, (2020)
Court’s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation. While prior text-to-text natural language generation (NLG) approaches can be used to addre...
Cited by0BibtexViews110DOI
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Xingyi Duan, Baoxin Wang, Ziyue Wang,Wentao Ma,Yiming Cui, Dayong Wu,Shijin Wang, Ting Liu, Tianxiang Huo, Zhen Hu, Heng Wang,Zhiyuan Liu
CCL, pp.439-451, (2019)
We present the first Chinese judicial reading comprehension dataset
Cited by11BibtexViews164DOI
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Xiao Chaojun,Zhong Haoxi, Guo Zhipeng,Tu Cunchao,Liu Zhiyuan,Sun Maosong, Zhang Tianyang,Han Xianpei, hu Zhen, Wang Heng, Xu Jianfeng
We propose a new dataset, CAIL2019-Similar Case Matching, which focuses on the task of similar case matching in the legal domain
Cited by5BibtexViews140
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Huajie Chen,Deng Cai, Wei Dai, Zehui Dai, Yadong Ding
EMNLP/IJCNLP (1), pp.6361-6366, (2019)
Experiments show that our model significantly improves the accuracy of charge-based prison term prediction, as well as the total term prediction
Cited by3BibtexViews159DOI
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IJCAI, (2019): 4085-4091
We propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks
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Congqing He, Li Peng, Yuquan Le, Jiawei He,Xiangyu Zhu
arXiv: Artificial Intelligence, (2019): 227-239
We focus on the few-shot problem of charge prediction according to the fact descriptions of criminal cases
Cited by2BibtexViews83DOI
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Thomas Vacek,Dezhao Song,Hugo Molina-Salgado, Ronald Teo, Conner Cowling,Frank Schilder
north american chapter of the association for computational linguistics, (2019)
Future research will focus on exploring machine learning methods and transfer learning techniques in order to apply our findings to other jurisdictions as well
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Laura Manor,Junyi Jessy Li
arXiv: Computation and Language, (2019)
We propose the task of summarizing legal documents in plain English and present an initial evaluation dataset for this task
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Ilias Chalkidis, Manos Fergadiotis,Prodromos Malakasiotis,Ion Androutsopoulos
ACL (1), pp.6314-6322, (2019)
EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand, which has been widely used in LMTC research, but is less than half the size of EURLEX57K and more than ten years old
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pp.275-282 (2019)
We have achieved the best performance compared to other participants of Competition on Legal Information Extraction/Entailment 2019 on the test set of the legal case retrieval task
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Wei Duan, Lin Li
We prove that the approach can improve the performance of the deep learning model in the multi-label charge prediction task
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Ilias Chalkidis,Ion Androutsopoulos, Nikolaos Aletras
Meeting of the Association for Computational Linguistics, (2019)
As already noted in Section 3, the distribution of importance scores is highly skewed in favour of the majority class, MAJORITY can correctly predict the score in most cases with zero mean absolute error
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EMNLP, pp.3540-3549, (2018)
We focus on the task of legal judgment prediction and address multiple subtasks of judgment predication with a topological learning framework
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COLING, pp.487-498, (2018)
We focus on the task of charge prediction according to the fact descriptions of criminal cases
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north american chapter of the association for computational linguistics, (2018)
We propose a novel task of court view generation and formulate it as a text-to-text natural language generation problem
Cited by26BibtexViews107DOI
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COLING (Demos), pp.146-151, (2018)
We propose a neural based system to jointly extract readable rationales and elevate charge prediction accuracy by a rationale augment mechanism
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Xianggen Liu,Lili Mou, Haotian Cui, Zhengdong Lu,Sen Song
IJCAI, (2018): 4237-4243
Experiments show that JUMPER achieves comparable or higher performance than baselines; that it reduces text reading by a large extent; and that it can find the key rationale if the information is local within a sentence
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