基于多粒度对抗训练的鲁棒跨语言对话系统

向露; 朱军楠; 周玉; 宗成庆

doi:10.16383/j.aas.c200764

基于多粒度对抗训练的鲁棒跨语言对话系统

doi: 10.16383/j.aas.c200764

向露^{1, 2,},
朱军楠^{1, 2,},
周玉^{1, 2, 3,},
宗成庆^{1, 2,}

1.
中国科学院自动化研究所模式识别国家重点实验室北京 100190
2.
中国科学院大学人工智能学院北京 100049
3.
凡语AI研究院, 北京中科凡语科技有限公司北京 100080

基金项目: 国家重点研发计划重点专项(2017YFB1002103)资助

详细信息

作者简介:
向露：中国科学院自动化研究所模式识别国家重点实验室博士研究生. 主要研究方向为人机对话系统, 文本生成和自然语言处理. E-mail: lu.xiang@nlpr.ia.ac.cn

朱军楠：中国科学院自动化研究所助理研究员. 主要研究方向为自动摘要, 文本生成和自然语言处理. E-mail: junnan.zhu@nlpr.ia.ac.cn

周玉：中国科学院自动化研究所研究员. 主要研究方向为自动摘要, 机器翻译和自然语言处理. 本文通信作者. E-mail: yzhou@nlpr.ia.ac.cn

宗成庆：中国科学院自动化研究所研究员, 中国科学院大学岗位教授, 中国计算机学会会士、中国人工智能学会会士. 主要研究方向为自然语言处理, 机器翻译.E-mail: cqzong@nlpr.ia.ac.cn

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出版历程
- 收稿日期: 2020-09-16
- 录用日期: 2021-01-15
- 网络出版日期: 2021-02-02
- 刊出日期: 2021-08-20

Robust Cross-lingual Dialogue System Based on Multi-granularity Adversarial Training

XIANG Lu^{1, 2
,},
ZHU Jun-Nan^{1, 2
,},
ZHOU Yu^{1, 2, 3
,},
ZONG Cheng-Qing^{1, 2
,}

1.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049
3.
Fanyu AI Laboratory, Beijing Fanyu Technology Co., Ltd, Beijing 100080

Funds: Supported by National Key Research and Development Program of China (2017YFB1002103)

More Information

Author Bio:
XIANG Lu　Ph. D. candidate at the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Her research interest covers dialogue systems, text generation, and natural language processing

ZHU Jun-Nan　Assistant professor at Institute of Automation, Chinese Academy of Sciences. His research interest covers summarization, text generation, and natural language processing

ZHOU Yu　Professor at Institute of Automation, Chinese Academy of Sciences. Her research interest covers summarization, machine translation, and natural language processing. Corresponding author of this paper

ZONG Cheng-Qing　Professor at Institute of Automation, Chinese Academy of Sciences, and an adjunct professor at the University of Chinese Academy of Sciences. He is CCF Fellow and CAAI Fellow. His research interest covers natural language processing and machine translation

摘要

摘要:
跨语言对话系统是当前国际研究的热点和难点. 在实际的应用系统搭建中, 通常需要翻译引擎作为不同语言之间对话的桥梁. 然而, 翻译引擎往往是基于不同训练样本构建的, 无论是所在领域, 还是擅长处理语言的特性, 均与对话系统的实际应用需求存在较大的差异, 从而导致整个对话系统的鲁棒性差、响应性能低. 因此, 如何增强跨语言对话系统的鲁棒性对于提升其实用性具有重要的意义. 提出了一种基于多粒度对抗训练的鲁棒跨语言对话系统构建方法. 该方法首先面向机器翻译构建多粒度噪声数据, 分别在词汇、短语和句子层面生成相应的对抗样本, 之后利用多粒度噪声数据和干净数据进行对抗训练, 从而更新对话系统的参数, 进而指导对话系统学习噪声无关的隐层向量表示, 最终达到提升跨语言对话系统性能的目的. 在公开对话数据集上对两种语言的实验表明, 所提出的方法能够显著提升跨语言对话系统的性能, 尤其提升跨语言对话系统的鲁棒性.
- 多粒度噪声 /
- 对抗训练 /
- 鲁棒性 /
- 跨语言 /
- 对话系统
Abstract:
Cross-lingual dialogue system is attractive and also very difficult for current international research. In real-word system implementations, machine translation engines are often needed to serve as a bridge for different languages. However, machine translation engines are often built based on different training data. No matter the field they focus on, or the characteristics of the language that they are expert at processing, there still remains huge gaps to the demands of the dialogue system, resulting in poor robustness and low response performance of the entire dialogue system. Therefore, how to improve the robustness of the cross-lingual dialogue system has very important realistic significance for boosting its practicability. This paper proposes a robust cross-lingual dialogue system building method based on multi-granularity adversarial training. This method firstly constructs multi-granularity noise data oriented to machine translation, generating corresponding adversarial samples at the word, phrase, and sentence level. Then, the adversarial training over multi-granularity noise data and clean data is adopted to update the parameters of the dialogue system, guiding the dialogue system to learn noise-independent hidden representations, so as to improve the performance of the cross-lingual dialogue system. Experimental results on publicly available dialogue datasets and two language pairs show that the proposed method can significantly improve the performance of the cross-lingual dialogue system, especially the robustness of the cross-lingual dialogue system.
- Multi-granularity noise /
- adversarial training /
- robust /
- cross-lingual /
- dialogue system

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图 1 基于机器翻译的跨语言对话系统

Fig. 1 Machine translation based cross-lingual dialogue system

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图 2 TSCP框架

Fig. 2 TSCP framework

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图 3 词汇级和短语级对抗样本生成框架

Fig. 3 The framework of word-level and phrase-level adversarial examples generation

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图 4 多粒度对抗样本实例

Fig. 4 An example of multi-granularity adversarial examples

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图 5 对抗训练结构框图

Fig. 5 The structure of adversarial training

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图 6 两种测试

Fig. 6 Two kinds of test

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表 1 数据集统计信息

Table 1 Statistics of datasets

数据集	CamRest676
规模	训练集: 405 验证集: 135 测试集: 136
领域	餐馆预定

数据集	KVRET
规模	训练集: 2425 验证集: 302 测试集: 302
领域	日程规划、天气信息查询、导航

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表 2 CamRest676数据集上的实验结果

Table 2 Experimental results on CamRest676

	对抗样本	Cross-test				Mono-test
	对抗样本	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数
0	基线系统	0.1731	0.4776	0.6485	0.7361	0.2001	0.9328	0.8204	1.0767
1	随机交换	0.1759	0.4851	0.6599	0.7484	0.2159	0.9104	0.7639	1.0530
2	停用词	0.1692	0.5000	0.6347	0.7365	0.2300	0.9179	0.7803	1.0791
3	同义词	0.1805	0.4403	0.7051	0.7532	0.2159	0.9030	0.7824	1.0586
4	词汇级	0.1941	0.4552	0.7503	0.7969	0.2056	0.8955	0.8227	1.0647
5	短语级	0.2017	0.4478	0.7602	0.8057	0.2215	0.8507	0.7992	1.0465
6	句子级	0.1937	0.4925	0.7662	0.8231	0.2127	0.8731	0.8121	1.0553
7	多粒度	0.2178	0.5149	0.7925	0.8715	0.2343	0.8881	0.8269	1.0918

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表 3 KVRET数据集上的实验结果

Table 3 Experimental results on KVRET

	对抗样本	Cross-test				Mono-test
	对抗样本	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数
0	基线系统	0.1737	0.4218	0.7073	0.7382	0.2096	0.7929	0.7948	1.0034
1	随机交换	0.1751	0.4436	0.7122	0.7531	0.2056	0.8400	0.8033	1.0273
2	停用词	0.1676	0.4327	0.7183	0.7431	0.1961	0.8109	0.8016	1.0023
3	同义词	0.1680	0.4145	0.7234	0.7370	0.1944	0.8109	0.7898	0.9947
4	词汇级	0.1805	0.4436	0.7696	0.7871	0.2095	0.8109	0.8202	1.0251
5	短语级	0.1933	0.4727	0.7603	0.8097	0.2219	0.8255	0.8170	1.0431
6	句子级	0.1803	0.4727	0.7843	0.8088	0.1965	0.8218	0.8136	1.0142
7	多粒度	0.1762	0.5236	0.7859	0.8309	0.1944	0.8218	0.8235	1.0171

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表 4 KVRET数据集上德到英的跨语言实验结果

Table 4 Cross-lingual experimental results from German to English on KVRET

	对抗样本	Cross-test				Mono-test
	对抗样本	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数
0	基线系统	0.1641	0.3964	0.7162	0.7203	0.2096	0.7927	0.7948	1.0034
1	词汇级	0.1661	0.4618	0.7596	0.7768	0.2011	0.8109	0.8163	1.0147
2	短语级	0.1842	0.5127	0.7922	0.8367	0.1975	0.8218	0.8248	1.0208
3	句子级	0.1844	0.5091	0.7774	0.8277	0.2067	0.8291	0.8095	1.0260
4	多粒度	0.1743	0.5309	0.8117	0.8456	0.1905	0.7964	0.8186	0.9981

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表 5 跨语言对话实验结果实例

Table 5 Examples of cross-lingual experimental results

	Cross-test	Mono-test
CamRest676 实验结果实例
实例1
	User: 请推荐给我一些位于南方的价位适中的餐厅. MT: please recommend me some restaurant at the appropriate price in the south.	User: please suggest me some moderately priced restaurant in the south.
Gold_bspan: moderate south
Gold_response: i found two moderately priced restaurants in the south part of town. would you prefer food_SLOT or food_SLOT food?
基线系统	generated_bspan: south generated_response: there are no restaurants in the south part of town.	generated_bspan: moderate south generated_response: there are two restaurants in the south part of town.
多粒度	generated_bspan: moderate south generated_response: there are two moderately priced restaurants in the south part of town. would you like more information?	generated_bspan: moderate south generated_response: there are two moderately priced restaurants in the south part of town. would you like more information?

KVRET 实验结果实例
实例2
	Driver: 最近的停车场怎么走? MT: how can the latest car park go?	Driver: what are the direction to the closest parking garage?
Gold_bspan: parking garage Gold_response: the closest parking garage is poi_SLOT, located distance_SLOT away at address_SLOT.
基线系统	generated_bspan: ITEM_3 generated_response: there is a ITEM_0 one and poi_SLOT, distance_SLOT away with traffic_info_SLOT.	generated_bspan: parking garage generated_response: the nearest parking garage is poi_SLOT, distance_SLOT away.
多粒度	generated_bspan: parking garage generated_response: the closest parking garage is distance_SLOT away.	generated_bspan: parking garage generated_response: the closest parking garage is poi_SLOT.
实例3
	Driver: 安排 5 日上午 11 点与副会长会面. MT: arranged for him to meet with the vice president at 11am on the 5th.	Driver: schedule a conference with the vice president on the 5th at 11am.
Gold_bspan: 5th; vice president; conference; 11am Gold_response: conference scheduled for the 5th at 11am with the vice president.
基线系统	generated_bspan: 5th; vice president; vice president generated_response: the 5th is set the president to discus the president.	generated_bspan: 5th; vice president; conference; 11am generated_response: the 5th is with a vice president for the 5th at address_SLOT with a high of the 5th of the 5th of the 5th of the 5th of the 5th of the 5th is with the 5th.
多粒度	generated_bspan: 5th; vice president; conference; 11am generated_response: your conference with vice president at this 5th at with the vice president at the 5th.	generated_bspan: 5th; vice president; conference; 11am generated_response: you have a conference with the vice president on the 5th at this 5th.

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表 6 翻译现象类别实例

Table 6 Categories of translation phenomena

类别 1	原始单语句子	I am looking for a *moderately* priced restaurant in the south part of town.
	中文测试集	你知道镇北部有什么*价格适中的*餐馆吗?
	MT	I＇ m looking for a *cheap* restaurant in the south of the town.
类别 2	原始单语句子	A restaurant in the *moderately* priced range, please.
	中文测试集	请给我一家*中等价位*的餐馆.
	MT	Please give me a *mid-priced* restaurant.
类别 3	原始单语句子	I would like a cheap restaurant that *serves* greek food.
	中文测试集	我想要一家供应希腊食物的便宜餐馆.
	MT	I＇ d like a cheap restaurant to *supply* greek food.

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表 7 翻译系统噪声类型分析

Table 7 Noise type analysis of machine translation

翻译结果分类	轮数
类别 1	27
类别 2	72
类别 3	23
类别 4	55

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表 8 4种翻译现象上的实验结果

Table 8 Experimental results on four translation phenomena

类别	Cross-test	Mono-test
类别	BLEU/ 实体匹配率/ 成功率${{F} }_{1}$	BLEU/ 实体匹配率/ 成功率${{F} }_{1}$
基线系统
1	0.1229/ 0.2632/ 0.3548	0.1987/ 1.0000/ 0.6571
2	0.1672/ 0.2879/ 0.4234	0.2093/ 0.9394/ 0.6239
3	0.1429/ 0.3500/ 0.5538	0.1588/ 0.8500/ 0.6757
4	0.1640/ 0.5909/ 0.5629	0.1891/ 0.8864/ 0.6595
多粒度
1	0.1706/ 0.4737/ 0.5135	0.2301/ 1.0000/ 0.6835
2	0.2327/ 0.5000/ 0.6748	0.2594/ 0.8939/ 0.6935
3	0.1607/ 0.3000/ 0.5352	0.1801/ 0.7000/ 0.5278
4	0.2066/ 0.5909/ 0.5989	0.1924/ 0.8182/ 0.6448

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表 9 CamRest676数据集上使用其他单语基线对话系统的跨语言实验结果

Table 9 Cross-lingual experimental results using other monolingual baseline dialogue systems on CamRest676

	对抗样本	Cross-test				Mono-test
	对抗样本	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数	BLEU	实体匹配率	成功率${{F} }_{1}$	组合分数
SEDST
0	基线系统	0.1671	0.6455	0.7294	0.8545	0.2107	0.9545	0.8120	1.0940
1	多粒度	0.2093	0.8333	0.8193	1.0356	0.2292	0.9259	0.8378	1.1111
LABES-S2S
2	基线系统	0.1910	0.7450	0.7260	0.9265	0.2350	0.9640	0.7990	1.1165
3	多粒度	0.2300	0.8150	0.8290	1.0520	0.2400	0.9440	0.8580	1.1410

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