The similar case matching task aims to detect which two cases are more similar for a given triplet. It plays a significant role in the legal industry and thus has gained much attention. Due to the rapid development of natural language processing technology, various deep learning techniques have been applied to similar case matching task and obtained attractive performance. Most existing researches usually focus on encoding legal documents into a continuous vector. However, a unified vector is difficult to model multiple elements of the case. In the real world, cases contain numerous elements, which are the basis for legal practitioners to judge the similarity among cases. Legal experts usually focus on whether the two cases have similar legal elements. It makes this task especially challenging. In this paper, we propose a novel model, namely Interactive Attention Capsule Network (dubbed as IACN). It attempts to simulate the process of judgment by legal experts, which captures fine-grained elements similarity to make an interpretable judgment. In other words, the IACN judges the similarity of the case pairs based on the legal elements. The more similar legal elements of a case pair, the higher the degree of similarity of the case pair. In addition, we devise an interactive dynamic routing mechanism, which can better learn the interactive representation of legal elements among cases than the vanilla dynamic routing. We conduct extensive experiments based on a real-world dataset. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.
AgirreE.BaneaC.CardieC.CerD.DiabM.Gonzalez-AgirreA.GuoW.Lopez-GazpioI.MaritxalarM.MihalceaR. et al., Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability, in: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015, pp. 252–263.
2.
AgirreE.BaneaC.CardieC.CerD.DiabM.Gonzalez-AgirreA.GuoW.MihalceaR.RigauG. and WiebeJ., Semeval-2014 task 10: Multilingual semantic textual similarity, in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 81–91.
3.
AgirreE.BaneaC.CerD.DiabM.Gonzalez AgirreA.MihalceaR.Rigau ClaramuntG. and WiebeJ., Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation, in: SemEval-2016. 10th International Workshop on Semantic Evaluation; 2016 Jun 16–17; San Diego, CA. Stroudsburg (PA): ACL; 2016. pp. 497–511, ACL (Association for Computational Linguistics), 2016.
4.
AgirreE.CerD.DiabM. and Gonzalez-AgirreA., Semeval-2012 task 6: A pilot on semantic textual similarity, in: * SEM 2012: The First Joint Conference on Lexical and Computational Semantics–Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), 2012, pp. 385–393.
5.
AgirreE.CerD.DiabM.Gonzalez-AgirreA. and GuoW., * sem 2013 shared task: Semantic textual similarity, in: Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 2013, pp. 32–43.
6.
AizawaA., An information-theoretic perspective of tf-idf measures, Information Processing & Management39(1) (2003), 45–65.
7.
Baeza-YatesR.Ribeiro-NetoB. et al., Modern information retrieval, Volume 463, ACM press New York, 1999.
8.
BleiD.M.NgA.Y. and JordanM.I., Latent dirichlet allocation, The Journal of Machine Learning Research3 (2003), 993–1022.
9.
BowmanS.R.AngeliG.PottsC. and ManningC.D., A large annotated corpus for learning natural language inference, arXiv preprint arXiv:1508.05326, 2015.
10.
BromleyJ.GuyonI.LeCunY.SäckingerE. and ShahR., Signature verification using a “siamese” time delay neural network, in: Advances in Neural Information Processing Systems, 1994, pp. 737–744.
11.
CardellinoC.TeruelM.AlemanyL. and VillataS., Legal nerc with ontologies, wikipedia and curriculum learning, 2017.
12.
ConneauA.KielaD.SchwenkH.BarraultL. and BordesA., Supervised learning of universal sentence representations from natural language inference data, arXiv preprint arXiv:1705.02364, 2017.
13.
DevlinJ.ChangM.-W.LeeK. and ToutanovaK., Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805, 2018.
14.
DevlinJ.ChangM.-W.LeeK. and ToutanovaK., Bert: Pre-training of deep bidirectional transformers for language understanding, in: Roceedings 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, pp. 4171–4186.
15.
DongX. and ShenJ., Triplet loss in siamese network for object tracking, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 459–474.
16.
FanY.PangL.HouJ.GuoJ.LanY. and ChengX., Matchzoo: A toolkit for deep text matching, arXiv preprint arXiv:1707.07270, 2017.
17.
HacheyB. and GroverC., Sentence extraction for legal text summarisation, in: IJCAI, 2005, pp. 1686–1687.
18.
HeC.PengL.LeY.HeJ. and ZhuX., SECaps: A sequence enhanced capsule model for charge prediction, in: TetkoI.V.KůrkováV.KarpovP. and TheisF., eds, Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, Cham, 2019, pp. 227–239. Springer International Publishing.
19.
HirschmanL. and GaizauskasR., Natural language question answering: the view from here, Natural Language Engineering7(4) (2001), 275.
20.
HuB.LuZ.LiH. and ChenQ., Convolutional neural network architectures for matching natural language sentences, in: Advances in Neural Information Processing Systems, 2014, pp. 2042–2050.
21.
HuB.LuZ.LiH. and ChenQ., Convolutional neural network architectures for matching natural language sentences, arXiv preprint arXiv:1503.03244, 2015.
22.
HuZ.LiX.TuC.LiuZ. and SunM., Few-shot charge prediction with discriminative legal attributes, in: Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 487–498.
23.
HuangP.-S.HeX.GaoJ.DengL.AceroA. and HeckL., Learning deep structured semantic models for web search using clickthrough data, in: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 2013, pp. 2333–2338.
24.
JiangJ.-Y.ZhangM.LiC.BenderskyM.GolbandiN. and NajorkM., Semantic text matching for long-form documents, in: The World Wide Web Conference, 2019, pp. 795–806.
25.
KeownR., Mathematical models for legal prediction, Computer/LJ2 (1980), 829.
26.
KimY., Convolutional neural networks for sentence classification, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, Oct. 2014, pp. 1746–1751. Association for Computational Linguistics.
27.
KortF., Predicting supreme court decisions mathematically: A quantitative analysis of the i∘ right to counsel i± cases, American Political Science Review51(1) (1957), 1–12.
28.
LeY.HeC.ChenM.WuY.HeX. and ZhouB., Learning to predict charges for legal judgment via self-attentive capsule network.
29.
LeY.WangZ.-J.QuanZ.HeJ. and YaoB., Acv-tree: A new method for sentence similarity modeling, in: IJCAI, 2018, pp. 4137–4143.
30.
LeeD.L.ChuangH. and SeamonsK., Document ranking and the vector-space model, IEEE Software14(2) (1997), 67–75.
31.
LiuB.NiuD.WeiH.LinJ.HeY.LaiK. and XuY., Matching article pairs with graphical decomposition and convolutions, in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 6284–6294.
32.
LuoB.FengY.XuJ.ZhangX. and ZhaoD., Learning to predict charges for criminal cases with legal basis, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, Sept. 2017, pp. 2727–2736. Association for Computational Linguistics.
33.
ManningC.D.SchützeH. and RaghavanP., Introduction to information retrieval, Cambridge university press, 2008.
34.
MikolovT.SutskeverI.ChenK.CorradoG.S. and DeanJ., Distributed representations of words and phrases and their compositionality, in: NIPS, 2013, pp. 3111–3119.
35.
MitraB.DiazF. and CraswellN., Learning to match using local and distributed representations of text for web search, in: Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1291–1299.
36.
NagelS.S., Applying correlation analysis to case prediction, Tex. L. Rev.42 (1963), 1006.
37.
PangL.LanY.GuoJ.XuJ.WanS. and ChengX., Text matching as image recognition, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30, 2016.
38.
PenningtonJ.SocherR. and ManningC.D., Glove: Global vectors for word representation, in: EMNLP, 2014, pp. 1532–1543.
39.
RobertsonS. and ZaragozaH., The probabilistic relevance framework: BM25 and beyond, Now Publishers Inc, 2009.
40.
SabourS.FrosstN. and HintonG., Matrix capsules with em routing, in: 6th International Conference on Learning Representations, ICLR, 2018.
41.
SabourS.FrosstN. and HintonG.E., Dynamic routing between capsules, in: GuyonI.LuxburgU.V.BengioS.WallachH.FergusR.VishwanathanS.GarnettR., eds, Advances in Neural Information Processing Systems 30, Curran Associates, Inc., 2017, pp. 3856–3866.
42.
SaltonG. and BuckleyC., Term-weighting approaches in automatic text retrieval, Information Processing & Management24(5) (1988), 513–523.
43.
SaltonG.WongA. and YangC.-S., A vector space model for automatic indexing, Communications of the ACM18(11) (1975), 613–620.
44.
SeoM.KembhaviA.FarhadiA. and HajishirziH., Bidirectional attention flow for machine comprehension, arXiv preprint arXiv:1611.01603, 2016.
45.
ShenY.HeX.GaoJ.DengL. and MesnilG., Learning semantic representations using convolutional neural networks for web search, in: Proceedings of the 23rd International Conference on World Wide Web, 2014, pp. 373–374.
46.
Van OpijnenM. and SantosC., On the concept of relevance in legal information retrieval, Artificial Intelligence and Law25(1) (2017), 65–87.
47.
WaghR.S. and AnandD., Legal document similarity: A multi-criteria decision-making perspective, PeerJ Computer Science6 (2020), e262–e262.
48.
WanS.LanY.GuoJ.XuJ.PangL. and ChengX., A deep architecture for semantic matching with multiple positional sentence representations, in: Thirtieth AAAI Conference on Artificial Intelligence, 2016.
49.
WangZ.HamzaW. and FlorianR., Bilateral multi-perspective matching for natural language sentences, arXiv preprint arXiv:1702.03814, 2017.
50.
XiaoC.ZhongH.GuoZ.TuC.LiuZ.SunM.ZhangT.HanX.WangH.XuJ. et al., CAIL2019-SCM: A dataset of similar case matching in legal domain, arXiv preprint arXiv:1911.08962, 2019.
51.
YeH.JiangX.LuoZ. and ChaoW., Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions, arXiv preprint arXiv:1802.08504, 2018.
52.
YinW.SchützeH.XiangB. and ZhouB., Abcnn: Attention-based convolutional neural network for modeling sentence pairs, Transactions of the Association for Computational Linguistics4 (2016), 259–272.
53.
ZhangX.LiP.JiaW. and ZhaoH., Multi-labeled relation extraction with attentive capsule network, Proceedings of the AAAI Conference on Artificial Intelligence33 (07 2019), 7484–7491.
54.
ZhongH.GuoZ.TuC.XiaoC.LiuZ. and SunM., Legal judgment prediction via topological learning, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 3540–3549.
55.
ZhongH.XiaoC.TuC.ZhangT.LiuZ. and SunM., 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, 2020, pp. 5218–5230.
56.
ZhongH.XiaoC.TuC.ZhangT.LiuZ. and SunM., Jec-qa: A legal-domain question answering dataset, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 9701–9708.
