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JNLP Team: Deep Learning for Legal Processing in COLIEE 2020

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arxiv 2011.08071 v1 pith:ST4ASKMD submitted 2020-11-04 cs.CL cs.IRcs.LG

Ha-Thanh Nguyen , Hai-Yen Thi Vuong , Phuong Minh Nguyen , Binh Tran Dang , Quan Minh Bui , Sinh Trong Vu , Chau Minh Nguyen , Vu Tran
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Ken Satoh Minh Le Nguyen
This is my paper · ORCID
classification cs.CLcs.IRcs.LG
keywords legalapproachcolieedatadeepdomainlearningproblems
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020. These systems are all characterized by being pre-trained on large amounts of data before being finetuned for the specified tasks. This approach helps to overcome the data scarcity and achieve good performance, thus can be useful for tackling related problems in information retrieval, and decision support in the legal domain. Besides, the approach can be explored to deal with other domain specific problems.

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  1. Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval via Bagging and SVR Ensembles

    cs.IR 2025-01 conditional novelty 3.0 of 10

    An ensemble of support vector regressors over embedding-space neighbors reaches recall 0.849 on German legal passage retrieval, higher than the reported GerDaLIR baselines.

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