Elamur Research
☰
home / papers / 2020 / How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2020.acl-main.466.pdf

Summary

This ACL 2020 survey maps Legal Artificial Intelligence (LegalAI), where NLP is used to reduce the document-heavy workload of lawyers and judges and to give laypeople low-cost legal guidance. The authors contrast two research traditions. Embedding-based approaches, such as word and concept embeddings and pretrained language models, learn latent features from large corpora and usually score better. Symbol-based approaches, such as information extraction, element detection and event timelines, rely on explicit legal knowledge and are easier to explain. The authors identify three open problems: knowledge modelling, reasoning that strictly follows legal rules, and interpretability. They take a close look at three applications (forecasting case outcomes, finding cases that resemble one another, and answering questions about the law) and run experiments on several datasets. Code and a curated list of datasets and papers are released on GitHub. A central finding is that current QA models fall far behind humans on bar-exam questions. The paper serves as a roadmap. It recommends combining both paradigms and keeping AI in a supporting, human-reviewed role to avoid harms such as gender or racial bias.

Key points

  • The legal QA experiments use JEC-QA, a bar-exam dataset with 28,641 multiple-choice/multiple-answer questions and 79,433 supporting articles. Questions are split into KD-Questions (knowledge-driven) and CA-Questions (case analysis).
  • The tested QA models (BiDAF, BERT, Co-matching, HAF) show a large gap to human performance. Almost all of them do better on case analysis than on knowledge questions, and none handle legal multi-hop reasoning well.
  • Other QA resources reviewed include CJRC, which uses the SQuAD 2.0 format with span, yes/no and unanswerable questions, and COLIEE, which has about 500 yes/no questions.
  • General-domain PLMs underperform on legal text because of its terminology. Zhong et al. (2019b) address this with a BERT-style model pretrained on Chinese civil and criminal case documents, and the survey compares it with other BERT variants.
  • Code is available at github.com/thunlp/CLAIM, and the dataset and paper list at github.com/thunlp/LegalPapers.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing · Law

Cited by

151 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 146 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.466

@inproceedings{zhong2020how,
  title = {{How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence}},
  author = {Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.466},
  url = {https://doi.org/10.18653/v1/2020.acl-main.466}
}

References

116 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Alan Akbik, Tanja Bergmann, and Roland Vollgraf. 2019. Pooled contextualized embeddings for named entity recognition. In Proceedings of NAACL. find
  2. Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preotiuc-Pietro, and Vasileios Lampos. 2016. Predicting judicial decisions of the european court of human rights: A natural language processing perspective. PeerJ Computer Science, 2. find
  3. Iosif ANGELIDIS, Ilias CHALKIDIS, and Manolis KOUBARAKIS. 2018. Named entity recognition, linking and generation for greek legislation. find
  4. Kevin D Ashley. 2017. Artificial intelligence and legal analytics: new tools for law practice in the digital age. Cambridge University Press. find
  5. Ngo Xuan Bach, Tran Ha Ngoc Thien, Tu Minh Phuong, et al. 2017. Question analysis for vietnamese legal question answering. In Proceedings of KSE. IEEE. find
  6. Deanna Barmakian. 2000. Better search engines for law. Law Libr. J., 92. find
  7. Roberto Bartolini, Alessandro Lenci, Simonetta Montemagni, Vito Pirrelli, and Claudia Soria. 2004. Semantic mark-up of Italian legal texts through NLP-based techniques. In Proceedings of LREC. find
  8. Paheli Bhattacharya, Kaustubh Hiware, Subham Rajgaria, Nilay Pochhi, Kripabandhu Ghosh, and Saptarshi Ghosh. 2019. A comparative study of summarization algorithms applied to legal case judgments. In Proceedings of ECIR. Springer. find
  9. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pages 2787–2795. find
  10. Mı́rian Bruckschen, Caio Northfleet, Paulo Bridi, Roger Granada, Renata Vieira, Prasad Rao, and Tomas Sander. 2010. Named entity recognition in the legal domain for ontology population. In Workshop Programme, page 16. Citeseer. find
  11. Davide Buscaldi, Paolo Rosso, José Manuel Gómez-Soriano, and Emilio Sanchis. 2010. Answering questions with an n-gram based passage retrieval engine. Journal of Intelligent Information Systems, 34(2):113–134. find
  12. Cristian Cardellino, Milagro Teruel, Laura Alonso Alemany, and Serena Villata. 2017. Legal NERC with ontologies, Wikipedia and curriculum learning. In Proceedings of EACL. find
  13. Ilias Chalkidis, Ion Androutsopoulos, and Nikolaos Aletras. 2019a. Neural legal judgment prediction in English. In Proceedings of ACL. find our card
  14. Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, and Ion Androutsopoulos. 2019b. Large-scale multi-label text classification on EU legislation. In Proceedings of ACL. find
  15. Ilias Chalkidis and Dimitrios Kampas. 2019. Deep learning in law: early adaptation and legal word embeddings trained on large corpora. Artificial Intelligence and Law, 27(2):171–198. find
  16. Huajie Chen, Deng Cai, Wei Dai, Zehui Dai, and Yadong Ding. 2019. Charge-based prison term prediction with deep gating network. In Proceedings of EMNLP-IJCNLP, pages 6363–6368. find
  17. Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event extraction via dynamic multi-pooling convolutional neural networks. In Proceedings of ACL. find
  18. Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2018. A walk-based model on entity graphs for relation extraction. In Proceedings of ACL, pages 81–88. find
  19. František Cvrček, Karel Pala, and Pavel Rychlý. 2012. Legal electronic dictionary for Czech. In Proceedings of LREC. find
  20. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL. ↗
  21. Phong-Khac Do, Huy-Tien Nguyen, Chien-Xuan Tran, Minh-Tien Nguyen, and Minh-Le Nguyen. 2017. Legal question answering using ranking svm and deep convolutional neural network. arXiv preprint arXiv:1703.05320. ↗
  22. Xingyi Duan, Baoxin Wang, Ziyue Wang, Wentao Ma, Yiming Cui, Dayong Wu, Shijin Wang, Ting Liu, Tianxiang Huo, Zhen Hu, et al. 2019. Cjrc: A reliable human-annotated benchmark dataset for chinese judicial reading comprehension. In Proceedings of CCL. Springer. find
  23. Biralatei Fawei, Adam Wyner, and Jeff Pan. 2016. Passing a USA national bar exam: a first corpus for experimentation. In Proceedings of LREC. find
  24. Biralatei Fawei, Adam Wyner, Jeff Z Pan, and Martin Kollingbaum. 2015. Using legal ontologies with rules for legal textual entailment. In AI Approaches to the Complexity of Legal Systems, pages 317–324. Springer. find
  25. Jianfeng Gao, Kristina Toutanova, and Wen-tau Yih. 2011. Clickthrough-based latent semantic models for web search. In Proceedings of SIGIR. ACM. find
  26. Anne von der Lieth Gardner. 1984. An artificial intelligence approach to legal reasoning. find
  27. Anton Geist. 2009. Using citation analysis techniques for computer-assisted legal research in continental jurisdictions. Available at SSRN 1397674. find
  28. Ben Hachey and Claire Grover. 2006. Extractive summarisation of legal texts. Artificial Intelligence and Law, 14(4):305–345. find
  29. Hiroaki Hayashi, Zecong Hu, Chenyan Xiong, and Graham Neubig. 2019. Latent relation language models. arXiv preprint arXiv:1908.07690. ↗
  30. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8). find
  31. Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Proceedings of NIPS. find
  32. Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018. Few-shot charge prediction with discriminative legal attributes. In Proceedings of COLING. find
  33. Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of CIKM. ACM. find
  34. Jyun-Yu Jiang, Mingyang Zhang, Cheng Li, Michael Bendersky, Nadav Golbandi, and Marc Najork. 2019. Semantic text matching for long-form documents. In Proceedings of WWW. ACM. find
  35. Rie Johnson and Tong Zhang. 2017. Deep pyramid convolutional neural networks for text categorization. In Proceedings of ACL. find
  36. Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov. 2016. Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651. ↗ our card
  37. Santosh Kabbur, Xia Ning, and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In Proceedings of SIGKDD. ACM. find
  38. Shang Li, Hongli Zhang, Lin Ye, Xiaoding Guo, and Binxing Fang. 2019a. Mann: A multichannel attentive neural network for legal judgment prediction. IEEE Access. find
  39. Liangyi Kang, Jie Liu, Lingqiao Liu, Qinfeng Shi, and Dan Ye. 2019. Creating auxiliary representations from charge definitions for criminal charge prediction. arXiv preprint arXiv:1911.05202. ↗
  40. Yu Li, Tieke He, Ge Yan, Shu Zhang, and Hui Wang. 2019b. Using case facts to predict penalty with deep learning. In International Conference of Pioneering Computer Scientists, Engineers and Educators, pages 610–617. Springer. find
  41. Yoshinobu Kano, Mi-Young Kim, Masaharu Yoshioka, Yao Lu, Juliano Rabelo, Naoki Kiyota, Randy Goebel, and Ken Satoh. 2018. Coliee-2018: Evaluation of the competition on legal information extraction and entailment. In Proceedings of JSAI, pages 177–192. Springer. find
  42. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of AAAI. find
  43. R Keown. 1980. Mathematical models for legal prediction. Computer/LJ, 2:829. find
  44. Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. 2016. Neural relation extraction with selective attention over instances. In Proceedings of ACL. find
  45. Mi-Young Kim and Randy Goebel. 2017. Two-step cascaded textual entailment for legal bar exam question answering. In Proceedings of Articial Intelligence and Law. ACM. find
  46. Mi-Young Kim, Ying Xu, and Randy Goebel. 2015. A convolutional neural network in legal question answering. find
  47. Mi-Young Kim, Ying Xu, Randy Goebel, and Ken Satoh. 2013. Answering yes/no questions in legal bar exams. In Proceedings of JSAI, pages 199–213. Springer. find
  48. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of EMNLP. find our card
  49. Fred Kort. 1957. Predicting supreme court decisions mathematically: A quantitative analysis of the ”right to counsel” cases. American Political Science Review, 51(1):1–12. find
  50. Onur Kuru, Ozan Arkan Can, and Deniz Yuret. 2016. CharNER: Character-level named entity recognition. In Proceedings of COLING. find
  51. Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In Proceedings of NAACL. find
  52. Jörg Landthaler, Bernhard Waltl, Patrick Holl, and Florian Matthes. 2016. Extending full text search for legal document collections using word embeddings. In JURIX, pages 73–82. find
  53. Benjamin E Lauderdale and Tom S Clark. 2012. The supreme court’s many median justices. American Political Science Review, 106(4):847–866. find
  54. Alessandro Lenci, Simonetta Montemagni, Vito Pirrelli, and Giulia Venturi. 2009. Ontology learning from italian legal texts. Law, Ontologies and the Semantic Web, 188:75–94. find
  55. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019a. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. ↗
  56. Zhiyuan Liu, Cunchao Tu, and Maosong Sun. 2019b. Legal cause prediction with inner descriptions and outer hierarchies. In Proceedings of CCL, pages 573–586. Springer. find
  57. Daniel Locke and Guido Zuccon. 2018. A test collection for evaluating legal case law search. In Proceedings of SIGIR. ACM. find
  58. Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017. Learning to predict charges for criminal cases with legal basis. In Proceedings of EMNLP. find
  59. K Tamsin Maxwell and Burkhard Schafer. 2008. Concept and context in legal information retrieval. In Proceedings of JURIX. find
  60. Douglas L Medin. 2000. Psychology of learning and motivation: advances in research and theory. Elsevier. find
  61. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. ↗
  62. Makoto Miwa and Mohit Bansal. 2016. End-to-end relation extraction using lstms on sequences and tree structures. In Proceedings of ACL, pages 1105– 1116. find our card
  63. Alfredo Monroy, Hiram Calvo, and Alexander Gelbukh. 2008. Using graphs for shallow question answering on legal documents. In Mexican International Conference on Artificial Intelligence. Springer. find
  64. Alfredo Monroy, Hiram Calvo, and Alexander Gelbukh. 2009. Nlp for shallow question answering of legal documents using graphs. In Proceedings of CI-CLing. Springer. find
  65. Alfredo López Monroy, Hiram Calvo, Alexander Gelbukh, and Georgina Garcı́a Pacheco. 2013. Link analysis for representing and retrieving legal information. In Proceedings of CICLing, pages 380–393. Springer. find
  66. Stuart S Nagel. 1963. Applying correlation analysis to case prediction. Texas Law Review, 42:1006. find
  67. John J. Nay. 2016. Gov2Vec: Learning distributed representations of institutions and their legal text. In Proceedings of the First Workshop on NLP and Computational Social Science. find
  68. Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016. Joint event extraction via recurrent neural networks. In Proceedings of NAACL. find
  69. Thien Huu Nguyen and Ralph Grishman. 2018. Graph convolutional networks with argument-aware pooling for event detection. In Proceedings of AAAI. find
  70. Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward. 2016. Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(4). find
  71. Sicheng Pan, Tun Lu, Ning Gu, Huajuan Zhang, and Chunlin Xu. 2019. Charge prediction for multi-defendant cases with multi-scale attention. In CCF Conference on Computer Supported Cooperative Work and Social Computing. Springer. find
  72. K Raghav, P Krishna Reddy, and V Balakista Reddy. 2016. Analyzing the extraction of relevant legal judgments using paragraph-level and citation information. AI4JCArtificial Intelligence for Justice, page 30. find
  73. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don’t know: Unanswerable questions for SQuAD. In Proceedings of ACL. find our card
  74. Steffen Rendle. 2010. Factorization machines. In Proceedings of ICDM. IEEE. find
  75. Stephen E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In Proceedings of SIGIR. find
  76. Gerard Salton and Christopher Buckley. 1988. Term-weighting approaches in automatic text retrieval. Information processing & management. find
  77. Jeffrey A Segal. 1984. Predicting supreme court cases probabilistically: The search and seizure cases, 1962-1981. American Political Science Review, 78(4):891–900. find
  78. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603. ↗ our card
  79. Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of CIKM. ACM. find
  80. Yi Shu, Yao Zhao, Xianghui Zeng, and Qingli Ma. 2019. Cail2019-fe. Technical report, Gridsum. find
  81. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of EMNLP, pages 1532– 1543. find
  82. Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, and Madhavi Perera. 2018. Legal document retrieval using document vector embeddings and deep learning. In Proceedings of SAI. Springer. find
  83. Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365. ↗
  84. Harry Surden. 2018. Artificial intelligence and law: An overview. Ga. St. UL Rev. find
  85. Matthew E Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A Smith. 2019. Knowledge enhanced contextual word representations. In Proceedings of EMNLP-IJCNLP. find
  86. Xipeng Qiu and Xuanjing Huang. 2015. Convolutional neural tensor network architecture for community-based question answering. In Proceedings of IJCAI. find
  87. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8). find
  88. Ryosuke Taniguchi and Yoshinobu Kano. 2016. Legal yes/no question answering system using case-role analysis. In Proceedings of JSAI, pages 284–298. Springer. find
  89. Oanh Thi Tran, Bach Xuan Ngo, Minh Le Nguyen, and Akira Shimazu. 2013. Answering legal questions by mining reference information. In Proceedings of JSAI. Springer. find
  90. Vu Tran, Minh Le Nguyen, and Ken Satoh. 2019. Building legal case retrieval systems with lexical matching and summarization using a pre-trained phrase scoring model. In Proceedings of Artificial Intelligence and Law. ACM. find
  91. Maarten Truyens and Patrick Van Eecke. 2014. Legal aspects of text mining. In Proceedings of LREC. find
  92. Howard Turtle. 1995. Text retrieval in the legal world. Artificial Intelligence and Law, 3(1-2). find
  93. S Sidney Ulmer. 1963. Quantitative analysis of judicial processes: Some practical and theoretical applications. Law and Contemporary Problems, 28:164. find
  94. Thomas Vacek, Ronald Teo, Dezhao Song, Timothy Nugent, Conner Cowling, and Frank Schilder. 2019. Litigation analytics: Case outcomes extracted from US federal court dockets. In Proceedings of NLLP Workshop. find
  95. Tom Vacek and Frank Schilder. 2017. A sequence approach to case outcome detection. In Proceedings of Articial Intelligence and Law, pages 209– 215. ACM. find
  96. Marc Van Opijnen and Cristiana Santos. 2017. On the concept of relevance in legal information retrieval. Artificial Intelligence and Law, 25(1). find
  97. Hui Wang, Tieke He, Zhipeng Zou, Siyuan Shen, and Yu Li. 2019. Using case facts to predict accusation based on deep learning. In Proceedings of QRS-C, pages 133–137. IEEE. find
  98. Shuohang Wang, Mo Yu, Jing Jiang, and Shiyu Chang. 2018. A co-matching model for multi-choice reading comprehension. In Proceedings of ACL. find
  99. Wei Wu, Hang Li, and Jun Xu. 2013. Learning query and document similarities from click-through bipartite graph with metadata. In Proceedings of WSDM. ACM. find
  100. Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, et al. 2018. Cail2018: A large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478. ↗ our card
  101. Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Tianyang Zhang, Xianpei Han, Heng Wang, Jianfeng Xu, et al. 2019. Cail2019-scm: A dataset of similar case matching in legal domain. arXiv preprint arXiv:1911.08962. ↗
  102. Jun Xu, Hang Li, and Chaoliang Zhong. 2010. Relevance ranking using kernels. In Proceedings of AIRS. Springer. find
  103. Yukun Yan, Daqi Zheng, Zhengdong Lu, and Sen Song. 2017. Event identification as a decision process with non-linear representation of text. arXiv preprint arXiv:1710.00969. ↗
  104. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237. ↗ our card
  105. Hai Ye, Xin Jiang, Zhunchen Luo, and Wenhan Chao. 2018. Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions. In Proceedings of NAACL. find
  106. Wenpeng Yin, Hinrich Schütze, Bing Xiang, and Bowen Zhou. 2016. ABCNN: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics. find our card
  107. Xiaoxiao Yin, Daqi Zheng, Zhengdong Lu, and Ruifang Liu. 2018. Neural entity reasoner for global consistency in ner. arXiv preprint arXiv:1810.00347. ↗
  108. Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of EMNLP. find
  109. Ni Zhang, Yi-Fei Pu, Sui-Quan Yang, Ji-Liu Zhou, and Jin-Kang Gao. 2017. An ontological chinese legal consultation system. IEEE Access, 5:18250–18261. find
  110. Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019. ERNIE: Enhanced language representation with informative entities. In Proceedings of ACL. find
  111. Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2018. Legal judgment prediction via topological learning. In Proceedings of EMNLP. find our card
  112. Haoxi Zhong, Yuzhong Wang, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. 2020. Iteratively questioning and answering for interpretable legal judgment prediction. In Proceedings of AAAI. find
  113. Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. 2019a. Jec-qa: A legal-domain question answering dataset. arXiv preprint arXiv:1911.12011. ↗
  114. Haoxi Zhong, Zhengyan Zhang, Zhiyuan Liu, and Maosong Sun. 2019b. Open chinese language pre-trained model zoo. Technical report, Technical Report. Technical Report. find
  115. Haichao Zhu, Furu Wei, Bing Qin, and Ting Liu. 2018. Hierarchical attention flow for multiple-choice reading comprehension. In Proceedings of AAAI. find
  116. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575. ↗