面向中文文本分类的字符级对抗样本生成方法

张顺香; 吴厚月; 朱广丽; 许鑫; 苏明星

doi:10.11999/JEIT220563

面向中文文本分类的字符级对抗样本生成方法

doi: 10.11999/JEIT220563

1.
安徽理工大学计算机科学与工程学院淮南 232001
2.
合肥综合性国家科学中心人工智能研究院合肥 230088

基金项目: 国家自然科学基金(62076006)，安徽高校协同创新项目(GXXT-2021-008)，安徽省研究生科研项目(YJS20210402)

详细信息

作者简介:
张顺香：男，教授，研究方向为情感计算、人物关系挖掘

吴厚月：男，硕士生，研究方向为对抗样本生成、关系抽取

朱广丽：女，副教授，研究方向为情感计算、复杂网络分析

许鑫：男，硕士生，研究方向为自然语言处理、因果关系抽取

苏明星：男，硕士生，研究方向为自然语言处理、情感计算

通讯作者:
张顺香　sxzhang@aust.edu.cn

中图分类号: TN915.08; TP391.1
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-07
- 修回日期: 2022-07-09
- 网络出版日期: 2022-07-14
- 刊出日期: 2023-06-10

Character-level Adversarial Samples Generation Approach for Chinese Text Classification

1.
School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China

Funds: The National Natural Science Foundation of China (62076006), The University Synergy Innovation Program of Anhui Province (GXXT-2021-008), The Graduate Students Scientific Research Project of Anhui Province(YJS20210402)

摘要

摘要: 对抗样本生成是一种通过添加较小扰动信息，使得神经网络产生误判的技术，可用于检测文本分类模型的鲁棒性。目前，中文领域对抗样本生成方法主要有繁体字和同音字替换等，这些方法都存在对抗样本扰动幅度大，生成对抗样本质量不高的问题。针对这些问题，该文提出一种字符级对抗样本生成方法(PGAS)，通过对多音字进行替换可以在较小扰动下生成高质量的对抗样本。首先，构建多音字字典，对多音字进行标注；然后对输入文本进行多音字替换；最后在黑盒模式下进行对抗样本攻击实验。实验在多种情感分类数据集上，针对多种最新的分类模型验证了该方法的有效性。
- 对抗样本生成 /
- 文本分类 /
- 情感分类 /
- 多音字 /
- 字符级对抗样本
Abstract: Adversarial sample generation is a technique that makes the neural network produce misjudgments by adding small disturbance information. Which can be used to detect the robustness of text classification models. At present, the methods of sample generation in the Chinese domain include mainly traditional characters and homophones substitution, which have the problems of large disturbance amplitude of sample generation and low quality of sample generation. Polyphonic characters Generation Adversarial Sample (PGAS), a character-level countermeasure samples generation approach, is proposed in this paper. Which can generate high-quality adversarial samples with minor disturbance by replacing polyphonic characters. First, a polyphonic word dictionary to label polyphonic words is constructed. Then, the input text with polyphonic words is replaced. Finally, an adversarial sample attack experiment in the black-box model is conducted. Experiments on multiple sentiment classification datasets verify the effectiveness of the proposed method for a variety of the latest classification models.
- Anti-sample generation /
- Text classification /
- Sentimental classification /
- Polyphonic characters /
- Character-level adversarial samples

HTML全文

图 1 PGAS模型框架图

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图 2 PGAS算法替换向量描述样例

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图 3 字音1和字音2在坐标系中的转移

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图 4 多音字3种拼音变换情况

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图 5 不同实验方法生成对抗样本数量的WMD和IMD分布情况

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表 1 实验数据集

项目	酒店评论数据	微博评论数据	商品评论数据
任务类型	情感倾向性分类	情感倾向性分类	情感倾向性分类
分类数目	2	2	2
训练集(条)	4120	70000	42130
测试集(条)	1766	30000	18056
多音字数量(个)	2556952	7391456	6585441

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表 2 在酒店评论数据集上的对比试验结果(%)

测试模型	无修改	对比方法								本文方法
	无修改	WordHandling		CWordAttacker		DeepWordBug		FastWordBug		PGAS
	准确率	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度
SVM	76.35	72.19	4.16	71.03	5.32	69.18	7.17	70.15	6.20	52.36	23.99
LSTM	83.21	76.25	6.96	74.29	8.92	72.51	10.70	75.22	7.99	62.17	21.04
MemNet	77.12	70.31	6.81	72.59	4.53	70.15	6.97	69.19	7.93	58.63	18.49
IAN	86.31	81.25	5.06	83.26	3.05	78.32	7.99	78.29	8.02	64.92	21.39
AOA	79.91	71.26	8.65	73.29	6.62	68.25	11.66	70.53	9.38	60.15	19.76
AEN-GloVe	86.32	79.81	6.51	81.07	5.25	77.16	9.16	80.09	6.23	68.37	17.95
LSTM+SynATT	88.61	83.59	5.02	82.56	6.05	78.39	10.22	81.37	7.24	61.84	26.77
TD-GAT	78.36	72.20	6.16	73.21	5.15	72.19	6.17	71.24	7.12	60.23	18.13
ASGCN	82.97	77.18	5.79	77.41	5.56	71.05	11.92	73.08	9.89	61.08	21.89
CNN	82.36	74.21	8.15	76.38	5.98	69.91	12.45	69.51	12.85	59.39	22.97
pos-ACNN-CNN	76.28	70.15	6.13	72.53	3.75	68.25	8.03	66.19	10.09	58.18	18.10

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表 3 在微博评论数据集上的对比试验结果(%)

测试模型	无修改	对比方法								本文方法
	无修改	WordHandling		CWordAttacker		DeepWordBug		FastWordBug		PGAS
	准确率	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度
SVM	74.25	68.32	5.93	69.04	5.21	66.03	8.22	63.51	10.74	53.21	21.04
LSTM	79.66	72.58	7.08	71.22	8.44	68.25	11.41	69.79	9.87	59.74	19.92
MemNet	73.28	66.82	6.46	65.39	7.89	64.51	8.77	59.21	14.07	54.09	19.19
IAN	80.39	74.41	5.98	76.28	4.11	73.28	7.11	74.07	6.32	59.74	20.65
AOA	77.21	68.25	8.96	63.05	14.16	62.89	14.32	64.19	13.02	53.66	23.55
AEN-GloVe	85.31	74.29	11.02	73.08	12.23	74.85	10.46	76.28	9.03	66.23	19.08
LSTM+SynATT	89.07	72.14	16.93	75.44	13.63	77.60	11.47	81.18	7.89	67.04	22.03
TD-GAT	83.06	76.33	6.73	73.98	9.08	72.56	10.50	74.61	8.45	54.39	28.67
ASGCN	80.17	69.19	10.98	71.04	9.13	69.04	11.13	62.88	17.29	56.18	23.99
CNN	76.33	68.38	7.95	66.37	9.96	69.71	6.62	69.44	6.89	57.20	19.13
pos-ACNN-CNN	70.94	61.25	9.69	59.37	11.57	61.43	9.51	60.07	10.87	59.33	11.61

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表 4 在商品评论数据集上的对比试验结果(%)

测试模型	无修改	对比方法								本文方法
	无修改	WordHandling		CWordAttacker		DeepWordBug		FastWordBug		PGAS
	准确率	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度	准确率	降低幅度
SVM	73.28	64.21	9.07	66.07	7.21	63.19	10.09	65.03	8.25	54.64	18.64
LSTM	77.04	69.07	7.97	67.45	9.59	68.17	8.87	68.29	8.75	53.21	23.83
MemNet	82.36	73.85	8.51	71.04	11.32	73.22	9.14	72.94	9.42	63.02	19.34
IAN	74.07	62.25	11.82	66.38	7.69	65.31	8.76	65.83	8.24	56.41	17.66
AOA	78.25	69.44	8.81	68.51	9.74	67.14	11.11	68.07	10.18	54.20	24.05
AEN-GloVe	81.33	70.03	11.30	73.09	8.24	70.25	11.08	72.55	8.78	55.97	25.36
LSTM+SynATT	85.60	76.49	9.11	78.21	7.39	73.21	12.39	74.54	11.06	61.08	24.52
TD-GAT	84.92	72.17	12.75	75.60	9.32	75.09	9.83	74.60	10.32	62.04	22.88
ASGCN	83.64	76.05	7.59	79.03	4.61	74.32	9.32	76.59	7.05	64.29	19.35
CNN	75.91	63.21	12.70	64.24	11.67	65.39	10.52	65.02	10.89	59.31	16.60
pos-ACNN-CNN	86.49	72.71	13.78	77.30	9.19	79.02	7.47	72.74	13.75	68.03	18.46

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表 5 不同实验方法生成对抗样本数量的WMD和IMD分布情况(条)

项目	值	对比方法				本文方法
项目	值	WordHandling	CWordAttacker	DeepWordBug	FastWordBug	PGAS
WMD	0-0.2	21	36	12	7	2000
	0.2-0.4	623	380	272	253	0
	0.4-0.6	860	789	325	397	0
	0.6-0.8	256	473	531	529	0
	0.8-1	240	266	860	814	0
IMD	0-0.2	1630	1368	1409	1091	364
	0.2-0.4	341	269	468	680	1352
	0.4-0.6	29	182	90	197	235
	0.6-0.8	0	181	33	25	49
	0.8-1	0	0	0	7	0

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