@inproceedings{jiang-etal-2024-h,
title = "{H}-{L}egal{KI}: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering",
author = "Jiang, Yue and
Guan, Ziyu and
Zhao, Jie and
Zhao, Wei and
Yang, Jiaqi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.856",
doi = "10.18653/v1/2024.findings-emnlp.856",
pages = "14614--14625",
abstract = "Legal question answering (LQA) aims to bridge the gap between the limited availability of legal professionals and the high demand for legal assistance. Traditional LQA approaches typically either select the optimal answers from an answer set or extract answers from law texts. However, they often struggle to provide relevant answers to complex, real-world questions due to the rigidity of predetermined answers. Although recent advancements in legal large language models have shown some potential in enhancing answer relevance, they fail to address the multiple user-specific circumstances, i.e., factual details in questions. To address these issues, we (1) construct the first publicly available legal community question-answering (LegalCQA) dataset; and (2) propose a Hierarchical Legal Knowledge Integration (H-LegalKI) framework. LegalCQA is collected from two widely used legal forums for developing user-centered LQA models. For H-LegalKI, we design a legal knowledge retriever that gathers comprehensive legal knowledge based on both entire questions and individual sentences. And an answer generation model is designed to understand question- and sentence-level factual details and integrate corresponding legal knowledge in a hierarchical way. Additionally, we design a de-redundancy module to remove redundant legal knowledge. Experiments on LegalCQA demonstrate the superiority of our framework over competitive baselines.",
}
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<abstract>Legal question answering (LQA) aims to bridge the gap between the limited availability of legal professionals and the high demand for legal assistance. Traditional LQA approaches typically either select the optimal answers from an answer set or extract answers from law texts. However, they often struggle to provide relevant answers to complex, real-world questions due to the rigidity of predetermined answers. Although recent advancements in legal large language models have shown some potential in enhancing answer relevance, they fail to address the multiple user-specific circumstances, i.e., factual details in questions. To address these issues, we (1) construct the first publicly available legal community question-answering (LegalCQA) dataset; and (2) propose a Hierarchical Legal Knowledge Integration (H-LegalKI) framework. LegalCQA is collected from two widely used legal forums for developing user-centered LQA models. For H-LegalKI, we design a legal knowledge retriever that gathers comprehensive legal knowledge based on both entire questions and individual sentences. And an answer generation model is designed to understand question- and sentence-level factual details and integrate corresponding legal knowledge in a hierarchical way. Additionally, we design a de-redundancy module to remove redundant legal knowledge. Experiments on LegalCQA demonstrate the superiority of our framework over competitive baselines.</abstract>
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%0 Conference Proceedings
%T H-LegalKI: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering
%A Jiang, Yue
%A Guan, Ziyu
%A Zhao, Jie
%A Zhao, Wei
%A Yang, Jiaqi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F jiang-etal-2024-h
%X Legal question answering (LQA) aims to bridge the gap between the limited availability of legal professionals and the high demand for legal assistance. Traditional LQA approaches typically either select the optimal answers from an answer set or extract answers from law texts. However, they often struggle to provide relevant answers to complex, real-world questions due to the rigidity of predetermined answers. Although recent advancements in legal large language models have shown some potential in enhancing answer relevance, they fail to address the multiple user-specific circumstances, i.e., factual details in questions. To address these issues, we (1) construct the first publicly available legal community question-answering (LegalCQA) dataset; and (2) propose a Hierarchical Legal Knowledge Integration (H-LegalKI) framework. LegalCQA is collected from two widely used legal forums for developing user-centered LQA models. For H-LegalKI, we design a legal knowledge retriever that gathers comprehensive legal knowledge based on both entire questions and individual sentences. And an answer generation model is designed to understand question- and sentence-level factual details and integrate corresponding legal knowledge in a hierarchical way. Additionally, we design a de-redundancy module to remove redundant legal knowledge. Experiments on LegalCQA demonstrate the superiority of our framework over competitive baselines.
%R 10.18653/v1/2024.findings-emnlp.856
%U https://aclanthology.org/2024.findings-emnlp.856
%U https://doi.org/10.18653/v1/2024.findings-emnlp.856
%P 14614-14625
Markdown (Informal)
[H-LegalKI: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering](https://aclanthology.org/2024.findings-emnlp.856) (Jiang et al., Findings 2024)
ACL