@inproceedings{liu-etal-2023-ccl23-eval-ren,
title = "{CCL}23-Eval 任务6系统报告:基于{CLS}动态加权平均和数据增强的电信网络诈骗案件分类(System Report for {CCL}23-Eval Task 6:::Classification of Telecom {I}nternet Fraud Cases Based on {CLS} Dynamic Weighted Average and Data Augement)",
author = "Liu, Tianjun and
Zhang, Tianhua and
Song, Mengxiao and
Liu, Tingwen",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-3.19",
pages = "179--183",
abstract = "{``}电信网络诈骗领域的案件分类作为文本分类的一项落地应用,其目的是为相关案件进行智能化的分析,有助于公安部门掌握诈骗案件的特点,针对性的预防、制止、侦查。本文以此问题为基础,从模型设计、训练过程、数据增强三个方面进行了研究,通过CLS动态加权平均、Multi-Sample Dropout、对抗训练FGM、回译等方法显著提升了模型对诈骗案件描述的分类性能。{''}",
language = "Chinese",
}
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<title>CCL23-Eval 任务6系统报告:基于CLS动态加权平均和数据增强的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6:::Classification of Telecom Internet Fraud Cases Based on CLS Dynamic Weighted Average and Data Augement)</title>
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<abstract>“电信网络诈骗领域的案件分类作为文本分类的一项落地应用,其目的是为相关案件进行智能化的分析,有助于公安部门掌握诈骗案件的特点,针对性的预防、制止、侦查。本文以此问题为基础,从模型设计、训练过程、数据增强三个方面进行了研究,通过CLS动态加权平均、Multi-Sample Dropout、对抗训练FGM、回译等方法显著提升了模型对诈骗案件描述的分类性能。”</abstract>
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%0 Conference Proceedings
%T CCL23-Eval 任务6系统报告:基于CLS动态加权平均和数据增强的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6:::Classification of Telecom Internet Fraud Cases Based on CLS Dynamic Weighted Average and Data Augement)
%A Liu, Tianjun
%A Zhang, Tianhua
%A Song, Mengxiao
%A Liu, Tingwen
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F liu-etal-2023-ccl23-eval-ren
%X “电信网络诈骗领域的案件分类作为文本分类的一项落地应用,其目的是为相关案件进行智能化的分析,有助于公安部门掌握诈骗案件的特点,针对性的预防、制止、侦查。本文以此问题为基础,从模型设计、训练过程、数据增强三个方面进行了研究,通过CLS动态加权平均、Multi-Sample Dropout、对抗训练FGM、回译等方法显著提升了模型对诈骗案件描述的分类性能。”
%U https://aclanthology.org/2023.ccl-3.19
%P 179-183
Markdown (Informal)
[CCL23-Eval 任务6系统报告:基于CLS动态加权平均和数据增强的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6:::Classification of Telecom Internet Fraud Cases Based on CLS Dynamic Weighted Average and Data Augement)](https://aclanthology.org/2023.ccl-3.19) (Liu et al., CCL 2023)
ACL