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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

向露,  朱军楠,  周玉,  宗成庆.  基于多粒度对抗训练的鲁棒跨语言对话系统.  自动化学报,  2021,  47(8): 1855−1866 doi: 10.16383/j.aas.c200764
引用本文: 向露,  朱军楠,  周玉,  宗成庆.  基于多粒度对抗训练的鲁棒跨语言对话系统.  自动化学报,  2021,  47(8): 1855−1866 doi: 10.16383/j.aas.c200764
Xiang Lu,  Zhu Jun-Nan,  Zhou Yu,  Zong Cheng-Qing.  Robust cross-lingual dialogue system based on multi-granularity adversarial training.  Acta Automatica Sinica,  2021,  47(8): 1855−1866 doi: 10.16383/j.aas.c200764
Citation: Xiang Lu,  Zhu Jun-Nan,  Zhou Yu,  Zong Cheng-Qing.  Robust cross-lingual dialogue system based on multi-granularity adversarial training.  Acta Automatica Sinica,  2021,  47(8): 1855−1866 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
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

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

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:

    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.

  • 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

    图  2  TSCP框架

    Fig.  2  TSCP framework

    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

    图  4  多粒度对抗样本实例

    Fig.  4  An example of multi-granularity adversarial examples

    图  5  对抗训练结构框图

    Fig.  5  The structure of adversarial training

    图  6  两种测试

    Fig.  6  Two kinds of test

    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
    下载: 导出CSV

    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
    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0.17310.47760.64850.73610.20010.93280.82041.0767
    1随机交换0.17590.48510.65990.74840.21590.91040.76391.0530
    2停用词0.16920.50000.63470.73650.23000.91790.78031.0791
    3同义词0.18050.44030.70510.75320.21590.90300.78241.0586
    4词汇级0.19410.45520.75030.79690.20560.89550.82271.0647
    5短语级0.20170.44780.76020.80570.22150.85070.79921.0465
    6句子级0.19370.49250.76620.82310.21270.87310.81211.0553
    7多粒度0.21780.51490.79250.87150.23430.88810.82691.0918
    下载: 导出CSV

    表  3  KVRET数据集上的实验结果

    Table  3  Experimental results on KVRET

    对抗样本Cross-testMono-test
    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0.17370.42180.70730.73820.20960.79290.79481.0034
    1随机交换0.17510.44360.71220.75310.20560.84000.80331.0273
    2停用词0.16760.43270.71830.74310.19610.81090.80161.0023
    3同义词0.16800.41450.72340.73700.19440.81090.78980.9947
    4词汇级0.18050.44360.76960.78710.20950.81090.82021.0251
    5短语级0.19330.47270.76030.80970.22190.82550.81701.0431
    6句子级0.18030.47270.78430.80880.19650.82180.81361.0142
    7多粒度0.17620.52360.78590.83090.19440.82180.82351.0171
    下载: 导出CSV

    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-testMono-test
    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0.16410.39640.71620.72030.20960.79270.79481.0034
    1词汇级0.16610.46180.75960.77680.20110.81090.81631.0147
    2短语级0.18420.51270.79220.83670.19750.82180.82481.0208
    3句子级0.18440.50910.77740.82770.20670.82910.80951.0260
    4多粒度0.17430.53090.81170.84560.19050.79640.81860.9981
    下载: 导出CSV

    表  5  跨语言对话实验结果实例

    Table  5  Examples of cross-lingual experimental results

    Cross-testMono-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.
    下载: 导出CSV

    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?
    MTI' 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.
    MTPlease 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.
    MTI' d like a cheap restaurant to supply greek food.
    下载: 导出CSV

    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 127
    类别 272
    类别 323
    类别 455
    下载: 导出CSV

    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-testMono-test
    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
    10.1229/ 0.2632/ 0.35480.1987/ 1.0000/ 0.6571
    20.1672/ 0.2879/ 0.42340.2093/ 0.9394/ 0.6239
    30.1429/ 0.3500/ 0.55380.1588/ 0.8500/ 0.6757
    40.1640/ 0.5909/ 0.56290.1891/ 0.8864/ 0.6595
    多粒度
    10.1706/ 0.4737/ 0.51350.2301/ 1.0000/ 0.6835
    20.2327/ 0.5000/ 0.67480.2594/ 0.8939/ 0.6935
    30.1607/ 0.3000/ 0.53520.1801/ 0.7000/ 0.5278
    40.2066/ 0.5909/ 0.59890.1924/ 0.8182/ 0.6448
    下载: 导出CSV

    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-testMono-test
    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0.16710.64550.72940.85450.21070.95450.81201.0940
    1多粒度0.20930.83330.81931.03560.22920.92590.83781.1111
    LABES-S2S
    2基线系统0.19100.74500.72600.92650.23500.96400.79901.1165
    3多粒度0.23000.81500.82901.05200.24000.94400.85801.1410
    下载: 导出CSV
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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

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