Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 11/2024

16-01-2024 | Original Article

BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering

Authors: Yanling Li, Jiaye Wu, Xudong Luo

Published in: Neural Computing and Applications | Issue 11/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical.
previous article Correction: Aggregating efficient transformer and CNN networks using learnable fuzzy measure for breast tumor malignancy prediction in ultrasound images
next article Rainfall forecasting at long lead times for eastern Australia using artificial neural networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
1.
Bagherian-Marandi N, Ravanshadnia M, Akbarzadeh-T MR (2021) Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes. Artif Intell Law 29:453–484CrossRef
2.
Bastings J, Titov I, Aziz W, et al (2017) Graph convolutional encoders for syntax-aware neural machine translation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 1957–1967
3.
Carvalho DS, Nguyen MT, Tran CX, et al (2015) Lexical-morphological modelling for legal text analysis. In: New Frontiers in Artificial Intelligence: JSAI-isAI 2015, Lecture Notes in Computer Science, vol 10091. Springer, p 295–311
4.
Chen D, Fisch A, Weston J, et al (2017) Reading wikipedia to answer open-domain questions. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp 1870–1879
5.
Chen T, Van Durme B (2017) Discriminative information retrieval for question answering sentence selection. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pp 719–725
6.
Cui Y, Che W, Liu T, et al (2020) Revisiting pre-trained models for Chinese natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp 657–668
7.
De Cao N, Aziz W, Titov I (2019) Question answering by reasoning across documents with graph convolutional networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 2306–2317
8.
De Martino G, Pio G, Ceci M (2022) PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments. Artif Intell Law 30:359–390CrossRef
9.
Devlin J, Chang MW, Lee K, et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4171–4186
10.
11.
12.
13.
Fawei B, Pan JZ, Kollingbaum M, et al (2018) A methodology for a criminal law and procedure ontology for legal question answering. In: Proceedings of the Joint International Semantic Technology Conference, pp 198–214
14.
Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, pp 729–734
15.
Green Jr BF, Wolf AK, Chomsky C, et al (1961) Baseball: an automatic question-answerer. In: Proceedings of Western Joint IRE-AIEE-ACM Computer Conference, pp 219–224
16.
Guo ZX, Deng XL (2021) Intelligent identification method of legal case entity based on BERT-BiLSTM-CRF. J Beijing Univ Posts Telecommun 44(4):129–134MathSciNet
17.
Harabagiu S, Moldovan D, Clark C, et al (2003) Answer mining by combining extraction techniques with abductive reasoning. In: Proceedings of the 12th Text Retrieval Conference, pp 375–382
18.
Huang Q, Luo X (2018) State-of-the-art and development trend of artificial intelligence combined with law. Comput Sci 45(12):1–11 (In Chinese)ADS
19.
Humphreys L, Boella G, van der Torre L et al (2021) Populating legal ontologies using semantic role labelling. Artif Intell Law 29(2):171–211CrossRef
20.
Joshi M, Choi E, Weld DS, et al (2017) TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp 1601–1611
21.
Kano Y, Hoshino R, Taniguchi R (2017) Analyzable legal yes/no question answering system using linguistic structures. EPiC Series Comput 47:57–67
22.
Kano Y, Kim MY, Yoshioka M, et al (2018) COLIEE-2018: evaluation of the competition on legal information extraction and entailment. In: New Frontiers in Artificial Intelligence: JSAI-isAI 2018, Lecture Notes in Computer Science, vol 11717. Springer, p 177–192
23.
Kien PM, Nguyen HT, Bach NX, et al (2020) Answering legal questions by learning neural attentive text representation. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 988–998
24.
Kourtin I, Mbarki S, Mouloudi A (2020) A legal question answering ontology-based system. In: Formalising Natural Languages: Applications to Natural Language Processing and Digital Humanities: NooJ 2020, Communications in Computer and Information Science, vol 1389. Springer, p 218–229
25.
Liu B, Wu Y, Zhang F et al (2022) Query generation and buffer mechanism: towards a better conversational agent for legal case retrieval. Inform Process Manag 59(5):103051CrossRef
26.
Liu J, Wu J, Luo X (2021) Chinese judicial summarising based on short sentence extraction and GPT-2. In: Knowledge Science, Engineering and Management: KSEM 2021, Lecture Notes in Computer Science, vol 12816. Springer, p 376–393
27.
Liu L, Luo J (2018) A question answering system based on deep learning. In: Proceedings of the International Conference on Intelligent Computing, pp 173–181
28.
Liu Y, Luo X, Yang X (2019) Semantics and structure based recommendation of similar legal cases. In: Proceedings of the 14th International Conference on Intelligent Systems and Knowledge Engineering, pp 388–395
29.
Liu Z, Xiong C, Sun M, et al (2020) Fine-grained fact verification with kernel graph attention network. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 7342–7351
30.
Mandal A, Ghosh K, Ghosh S et al (2022) A sequence labelling model for catchphrase identification from legal case documents. Artif Intell Law 30:325–358CrossRef
31.
Marcheggiani D, Titov I (2017) Encoding sentences with graph convolutional networks for semantic role labelling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 1506–1515
32.
Martinez-Gil J, Freudenthaler B, Tjoa AM (2019) Multiple choice question answering in the legal domain using reinforced co-occurrence. In: Proceedings of the International Conference on Database and Expert Systems Applications, pp 138–148
33.
McElvain G, Sanchez G, Teo D, et al (2019) Non-factoid question answering in the legal domain. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1395–1396
34.
Qin L, Xu X, Che W, et al (2020) Dynamic fusion network for multi-domain end-to-end task-oriented dialogueue. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 6344–6354
35.
Robertson S, Zaragoza H (2009) The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc
36.
Šavelka J, Ashley KD (2022) Legal information retrieval for understanding statutory terms. Artif Intell Law 30:245–289CrossRef
37.
38.
Shao H, Chen Y, Huang S (2020) BERT-based ensemble model for statute law retrieval and legal information entailment. In: New Frontiers in Artificial Intelligence: JSAI-isAI 2020, Lecture Notes in Computer Science, vol 12758. Springer, p 226–239
39.
Silveira R, Fernandes CG, Neto JAM et al (2021) Topic modelling of legal documents via LEGAL-BERT. CEUR Workshop Proceedings 2896:64–72
40.
41.
Sun H, Dhingra B, Zaheer M, et al (2018) Open domain question answering using early fusion of knowledge bases and text. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 4231–4242
42.
Tagarelli A, Simeri A (2022) Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code. Artif Intell Law 30:417–473CrossRef
43.
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp 6000–6010
44.
Velickovic P, Cucurull G, Casanova A, et al (2018) Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations, pp 1–12
45.
46.
Voorhees EM et al (1999) The TREC-8 question answering track report. Trec 99:77–82
47.
Wang C, Luo X (2021) A legal question answering system based on bert. In: Proceedings of the 5th International Conference on Computer Science and Artificial Intelligence, pp 278–283
48.
Wang S, Jiang J (2017) Machine comprehension using match-LSTM and answer pointer. In: Proceedings of the 2017 International Conference on Learning Representations, pp 1–15
49.
Wang S, Yu M, Jiang J, et al (2018) A co-matching model for multi-choice reading comprehension. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp 746–751
50.
Wehnert S, Sudhi V, Dureja S, et al (2021) Legal norm retrieval with variations of the Bert model combined with TF-IDF vectorization. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp 285–294
51.
Wenestam A (2021) Labelling factual information in legal cases using fine-tuned BERT models. Master’s thesis, Uppsala University, Uppsala, Sweden
52.
Wu J, Luo X (2021) Alignment-based graph network for judicial examination task. In: Knowledge Science, Engineering and Management: KSEM 2021, Lecture Notes in Computer Science, vol 12817. Springer, p 386–400
53.
Xiao C, Hu X, Liu Z et al (2021) Lawformer: a pre-trained language model for chinese legal long documents. AI Open 2:79–84CrossRef
54.
Xu K, Wu L, Wang Z, et al (2018) SQL-to-text generation with graph-to-sequence model. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 931–936
55.
56.
Xu Y, Li T, Han Z (2020) The language model for legal retrieval and BERT-based model for rhetorical role labelling for legal judgments. CEUR Workshop Proceedings 2826:71–75
57.
Yu M, Yin W, Hasan KS, et al (2017) Improved neural relation detection for knowledge base question answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp 571–581
58.
Zhang NN, Xing Y (2021) Questions and answers on legal texts based on BERT-BiGRU. In: Journal of Physics: Conference Series, p article id. 012035, 10.1088/1742-6596/1828/1/012035
59.
Zhang Y, Qi P, Manning CD (2018) Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 2205–2215
60.
Zhong H, Xiao C, Tu C, et al (2020a) How does nlp benefit legal system: a summary of legal artificial intelligence. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 5218–5230
61.
Zhong H, Xiao C, Tu C, et al (2020b) JEC-QA: a legal-domain question answering dataset. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp 9701–9708
62.
Zhong Q, Fan X, Luo X et al (2019) An explainable multi-attribute decision model based on argumentation. Expert Syst Appl 117:42–61CrossRef
63.
Zhu H, Wei F, Qin B, et al (2018) Hierarchical attention flow for multiple-choice reading comprehension. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence, pp 6077–6084
Metadata
Title
BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering
Authors
Yanling Li
Jiaye Wu
Xudong Luo
Publication date
16-01-2024
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2024
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-09380-5

Other articles of this Issue 11/2024

Neural Computing and Applications 11/2024 Go to the issue

Original Article

Adapting transfer learning models to dataset through pruning and Avg-TopK pooling

Correction

Correction: Aggregating efficient transformer and CNN networks using learnable fuzzy measure for breast tumor malignancy prediction in ultrasound images

Original Article

Federated learning model for credit card fraud detection with data balancing techniques

Original Article

Classification of orbital tumors using convolutional neural networks

Original Article

Integrated fuzzy and deep learning model for identification of coconut maturity without human intervention

Original Article

ANN-based deep collocation method for natural convection in porous media

Premium Partner

    Image Credits