@inproceedings{liu-etal-2020-ji-yu,
title = "基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)",
author = "Liu, Haishun and
Wang, Lei and
Chen, Yanguang and
Zhang, Shuchen and
Sun, Yuanyuan and
Lin, Hongfei",
editor = "Sun, Maosong and
Li, Sujian and
Zhang, Yue and
Liu, Yang",
booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
month = oct,
year = "2020",
address = "Haikou, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2020.ccl-1.69",
pages = "743--753",
abstract = "案件要素识别指将案件描述中重要事实描述自动抽取出来,并根据领域专家设计的要素体系进行分类,是智慧司法领域的重要研究内容。基于传统神经网络的文本编码难以提取深层次特征,基于阈值的多标签分类难以捕获标签间依赖关系,因此本文提出了基于预训练语言模型的多标签文本分类模型。该模型采用以Layer-attentive策略进行特征融合的语言模型作为编码器,使用基于LSTM的序列生成模型作为解码器。在{``}CAIL2019{''}数据集上进行实验,该方法比基于循环神经网络的算法在F1值上最高可提升7.6{\%},在相同超参数设置下比基础语言模型(BERT)提升约3.2{\%}。",
language = "Chinese",
}
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<abstract>案件要素识别指将案件描述中重要事实描述自动抽取出来,并根据领域专家设计的要素体系进行分类,是智慧司法领域的重要研究内容。基于传统神经网络的文本编码难以提取深层次特征,基于阈值的多标签分类难以捕获标签间依赖关系,因此本文提出了基于预训练语言模型的多标签文本分类模型。该模型采用以Layer-attentive策略进行特征融合的语言模型作为编码器,使用基于LSTM的序列生成模型作为解码器。在“CAIL2019”数据集上进行实验,该方法比基于循环神经网络的算法在F1值上最高可提升7.6%,在相同超参数设置下比基础语言模型(BERT)提升约3.2%。</abstract>
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%0 Conference Proceedings
%T 基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)
%A Liu, Haishun
%A Wang, Lei
%A Chen, Yanguang
%A Zhang, Shuchen
%A Sun, Yuanyuan
%A Lin, Hongfei
%Y Sun, Maosong
%Y Li, Sujian
%Y Zhang, Yue
%Y Liu, Yang
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 October
%I Chinese Information Processing Society of China
%C Haikou, China
%G Chinese
%F liu-etal-2020-ji-yu
%X 案件要素识别指将案件描述中重要事实描述自动抽取出来,并根据领域专家设计的要素体系进行分类,是智慧司法领域的重要研究内容。基于传统神经网络的文本编码难以提取深层次特征,基于阈值的多标签分类难以捕获标签间依赖关系,因此本文提出了基于预训练语言模型的多标签文本分类模型。该模型采用以Layer-attentive策略进行特征融合的语言模型作为编码器,使用基于LSTM的序列生成模型作为解码器。在“CAIL2019”数据集上进行实验,该方法比基于循环神经网络的算法在F1值上最高可提升7.6%,在相同超参数设置下比基础语言模型(BERT)提升约3.2%。
%U https://aclanthology.org/2020.ccl-1.69
%P 743-753
Markdown (Informal)
[基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)](https://aclanthology.org/2020.ccl-1.69) (Liu et al., CCL 2020)
ACL