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基于全局覆盖机制与表示学习的生成式知识问答技术

刘琼昕 王亚男 龙航 王佳升 卢士帅

刘琼昕, 王亚男, 龙航, 王佳升, 卢士帅. 基于全局覆盖机制与表示学习的生成式知识问答技术. 自动化学报, 2022, 48(10): 2392−2405 doi: 10.16383/j.aas.c190785
引用本文: 刘琼昕, 王亚男, 龙航, 王佳升, 卢士帅. 基于全局覆盖机制与表示学习的生成式知识问答技术. 自动化学报, 2022, 48(10): 2392−2405 doi: 10.16383/j.aas.c190785
Liu Qiong-Xin, Wang Ya-Nan, Long Hang, Wang Jia-Sheng, Lu Shi-Shuai. Generative knowledge question answering technology based on global coverage mechanism and representation learning. Acta Automatica Sinica, 2022, 48(10): 2392−2405 doi: 10.16383/j.aas.c190785
Citation: Liu Qiong-Xin, Wang Ya-Nan, Long Hang, Wang Jia-Sheng, Lu Shi-Shuai. Generative knowledge question answering technology based on global coverage mechanism and representation learning. Acta Automatica Sinica, 2022, 48(10): 2392−2405 doi: 10.16383/j.aas.c190785

基于全局覆盖机制与表示学习的生成式知识问答技术

doi: 10.16383/j.aas.c190785
基金项目: 国家自然科学基金 (62072039) 资助
详细信息
    作者简介:

    刘琼昕:北京理工大学计算机学院副教授. 主要研究方向为人工智能, 自然语言处理, 具体研究知识推理, 关系抽取, 任务规划, 决策支持. 本文通信作者. E-mail: summer@bit.edu.cn

    王亚男:北京理工大学计算机学院硕士研究生. 主要研究方向为自然语言处理, 问答系统.E-mail: wyn1895@163.com

    龙航:北京理工大学计算机学院硕士研究生. 主要研究方向为自然语言处理, 表示学习, 问答系统.E-mail: longhang@ict.ac.cn

    王佳升:北京理工大学硕士研究生. 主要研究方向为深度学习, 自然语言处理和知识图谱.E-mail: 3120191049@bit.edu.cn

    卢士帅:北京理工大学计算机学院硕士研究生. 主要研究方向为自然语言处理领域的关系提取, 特别是关系提取中的小样本学习.E-mail: 3120191028@bit.edu.cn

Generative Knowledge Question Answering Technology Based on Global Coverage Mechanism and Representation Learning

Funds: Supported by National Natural Science Foundation of  China (62072039) 
More Information
    Author Bio:

    LIU Qiong-Xin Associate professor at the School of Computer Science and Technology, Beijing Institute of Technology. Her research interest covers artificial intelligence and natural language processing, specifically knowledge reasoning, relationship extraction, task planning, and decision support. Corresponding author of this paper

    WANG Ya-Nan Master student at the School of Computer Science and Technology, Beijing Institute of Technology. Her research interest covers natural language processing and question answering system

    LONG Hang Master student at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers natural language processing, representation learning, and question answering system

    WANG Jia-Sheng Master student at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers deep learning, natural language processing, and knowledge graphs

    LU Shi-Shuai Master student at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers relation extraction in the field of natural language processing, especially few-shot-learning in relation extraction

  • 摘要: 针对现有生成式问答模型中陌生词汇导致答案准确率低下的问题和模式混乱导致的词汇重复问题, 本文提出引入知识表示学习结果的方法提高模型识别陌生词汇的能力, 提高模型准确率. 同时本文提出使用全局覆盖机制以平衡不同模式答案生成的概率, 减少由预测模式混乱导致的重复输出问题, 提高答案的质量. 本文在知识问答模型基础上结合知识表示学习的推理结果, 使模型具备模糊回答的能力. 在合成数据集和现实世界数据集上的实验证明了本模型能够有效地提高生成答案的质量, 能对推理知识进行模糊回答.
  • 图  1  MCQA 模型图

    Fig.  1  The overall diagram of MCQA

    图  2  模型词典示意图

    Fig.  2  The diagram of vocabulary

    图  3  解码器工作机制示意图

    Fig.  3  The diagram of working mechanism of decoder

    图  4  MCQA (TE, CE)与CoreQA 答案对比样例

    Fig.  4  The comparison of MCQA (TE, CE) and CoreQA sample outputs

    图  5  社区问答样例

    Fig.  5  The sample outputsof community QA

    图  6  知识补全示意图

    Fig.  6  The diagram of knowledge base completion

    图  7  模糊问答样例

    Fig.  7  The sample outputs of ambiguously QA

    表  1  问答数据集规模

    Table  1  The size of QA datasets

    数据集 问答对数量 关系数量
    SimpleQuestions 101 754 1 631
    生日问答数据集 239 922 5
    社区问答数据集 505 021 4 011
    下载: 导出CSV

    表  2  SimpleQuestion 数据集实验结果

    Table  2  The experimental results of SimpleQuestion datasets

    方法 准确率 (%)
    BiCNN[13] 90.0
    AMPCNN[29] 91.3
    HR-BiLSTM[30] 93.3
    CoreQA 92.8
    MCQA (WE, CE) 93.8
    MCQA (TE, CE) 94.3
    下载: 导出CSV

    表  3  生日数据集实验结果 (%)

    Table  3  The experimental results of birthday datasets (%)

    方法 $ {{P}_{g}} $ $ {{P}_{y}} $ $ {{P}_{m}} $ $ {{P}_{d}} $ $ {{P}_{r}} $
    Seq2Seq 67.3 23.4 37.2
    NMT 71.6 27.1 54.7
    CopyNet 75.2 71.9
    GenQA (本文) 73.4 63.2 65.8 77.1 62.6
    CoreQA 75.6 84.8 93.4 81 80.3
    MCQA (WE, CE) 89.8 89.1 98.4 93.2 84.1
    MCQA (TE, CE) 88.6 89.4 98.7 93.6 84.6
    下载: 导出CSV

    表  4  社区问答实验结果 (%)

    Table  4  The experimental results of community QA datasets (%)

    方法 正确性 流畅性 一致性
    CopyNet 19.4 21.3
    GenQA (本文) 24.3 38.3 24.1
    CoreQA 49.3 51.8 62.5
    MCQA (WE, CE) 52.3 55.8 65.2
    MCQA (TE, CE) 54.1 56.3 65.0
    下载: 导出CSV

    表  5  模糊问答推理结果 (%)

    Table  5  The prediction results of ambiguously QA (%)

    方法 $ {{P}_{y}} $ $ {{P}_{m}} $ $ {{P}_{d}} $
    PTransE 93.2 97.4 95.0
    下载: 导出CSV

    表  6  模糊问答结果 (%)

    Table  6  The results of ambiguously QA (%)

    方法 $ {{F1}_{t}} $ $ {{P}_{y}} $ $ {{P}_{m}} $ $ {{P}_{d}} $ $ {{P}_{r}} $
    MCQA (WE, CE) 87.7 78.1 88.2 90.8 80.9
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-15
  • 录用日期:  2020-04-10
  • 网络出版日期:  2022-09-16
  • 刊出日期:  2022-10-14

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