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基于MHSA和句法关系增强的机器阅读理解方法研究

张虎 王宇杰 谭红叶 李茹

张虎, 王宇杰, 谭红叶, 李茹. 基于MHSA和句法关系增强的机器阅读理解方法研究. 自动化学报, 2021, x(x): 1−11 doi: 10.16383/j.aas.c200951
引用本文: 张虎, 王宇杰, 谭红叶, 李茹. 基于MHSA和句法关系增强的机器阅读理解方法研究. 自动化学报, 2021, x(x): 1−11 doi: 10.16383/j.aas.c200951
Zhang Hu, Wang Yu-Jie, Tan Hong-Ye, Li Ru. Research on machine reading comprehension method based on mhsa and syntactic relations enhancement. Acta Automatica Sinica, 2021, x(x): 1−11 doi: 10.16383/j.aas.c200951
Citation: Zhang Hu, Wang Yu-Jie, Tan Hong-Ye, Li Ru. Research on machine reading comprehension method based on mhsa and syntactic relations enhancement. Acta Automatica Sinica, 2021, x(x): 1−11 doi: 10.16383/j.aas.c200951

基于MHSA和句法关系增强的机器阅读理解方法研究

doi: 10.16383/j.aas.c200951
基金项目: 国家重点研发计划项目(2018YFB1005103); 国家自然科学基金重点项目(61936012); 山西省自然科学基金项目(201901D111028)资助
详细信息
    作者简介:

    张虎:山西大学计算机与信息技术学院副教授, 2014 年于山西大学计算机与信息技术学院获得工学博士学位, 主要研究方向为人工智能与自然语言处理. 本文通信作者. E-mail: zhanghu@sxu.edu.cn

    王宇杰:山西大学计算机与信息技术学院硕士研究生. 主要研究方向为自然语言处理. E-mail: init_wang@foxmail.com

    谭红叶:山西大学计算机与信息技术学院副教授, 2008年于哈尔滨工业大学计算机学院获得博士学位. 主要研究方向为: 人工智能、自然语言处理. Email: tanhongye@sxu.edu.cn

    李茹:山西大学计算机与信息技术学院教授, 2011年于山西大学计算机与信息技术学院获得工学博士学位. 主要研究方向为人工智能与自然语言处理. E-mail: liru@sxu.edu.cn

Research on Machine Reading Comprehension Method based on MHSA and Syntactic Relations Enhancement

Funds: Supported by National Key Research and Development Program of China(2018YFB1005103), Key Program of National Natural Science Foundation of China (61806117), Natural Science Foundation of Shanxi Province (201901D111028)
More Information
    Author Bio:

    ZHANG Hu Associate professor in the School of Computer and Information Technology, Shanxi University. He received his Ph.D. degree from the School of Computer and Information Technology, Shanxi University in 2014. His research interest covers AI and NLP. Corresponding author of this paper

    WANG Yu-Jie Master student in the School of Computer and Information Technology, Shanxi University. His research interest covers NLP

    TAN Hong-Ye Associate professor in the School of Computer and Information Technology, Shanxi University. She received her Ph.D. degree from the School of computer, Harbin Institute of Technology in 2008. Her research interest covers AI and NLP

    LI Ru Professor in the School of Computer and Information Technology, Shanxi University. She received her Ph.D. degree from the School of Computer and Information Technology, Shanxi University in 2011. Her research interest covers AI and NLP

  • 摘要: 机器阅读理解是自然语言处理领域中一项重要研究任务, 目标是通过机器理解给定的阅读材料和问题, 最终实现自动答题. 目前联合观点类问题解答和答案依据挖掘的多任务联合学习研究在机器阅读理解应用中受到广泛关注, 它可以同时给出问题答案和支撑答案的相关证据, 然而现有观点类问题的答题方法在答案线索识别上表现还不是太好, 已有答案依据挖掘方法仍不能较好捕获段落中词语之间的依存关系. 基于此, 本文引入多头自注意力(Multi-Head Self-Attention, MHSA)进一步挖掘阅读材料中观点类问题的文字线索, 改进了观点类问题的自动解答方法; 将句法关系融入到图构建过程中, 提出了基于关联要素关系图的多跳推理方法, 实现了答案支撑句挖掘; 通过联合优化两个子任务, 构建了基于多任务联合学习的阅读理解模型. 在2020中国“法研杯”司法人工智能挑战赛(China AI Law Challenge 2020, CAIL2020)和HotpotQA数据集上的实验结果表明, 本文提出的方法相比已有基线模型取得了更好的效果.
  • 图  1  CAIL2020阅读理解数据集样例

    Fig.  1  Sample of CAIL2020 MRC dataset

    图  2  MJL-Model模型结构

    Fig.  2  Model architecture of MJL-Model

    图  3  多跳推理层结构图

    Fig.  3  Model architecture of multi-hop reasoning layer

    图  4  注意力可视化样例

    Fig.  4  Sample of attention visualization

    图  5  关联要素关系图样例

    Fig.  5  Sample of related element graph

    图  6  多跳推理注意力可视化样例图

    Fig.  6  Visible sample of multi-hop reasoning attention

    表  1  CAIL2020数据集实验结果

    Table  1  Results on the CAIL2020 dataset

    ModelAns_F1Sup_F1Join_F1
    Baseline_BERT 70.40 65.74 49.25
    Baseline_RoBERTa 71.81 71.11 55.74
    Baseline_DPCNN 77.43 75.07 61.80
    Cola 74.63 73.68 59.62
    DFGN_CAIL 68.79 72.34 53.82
    MJL-Model 78.83 75.51 62.72
    下载: 导出CSV

    表  2  HotpotQA实验结果

    Table  2  Results on the HotpotQA dataset

    ModelAns_F1Sup_F1Joint_F1
    Baseline 58.28 66.66 40.86
    QFE 68.70 84.70 60.60
    DFGN 69.34 82.24 59.86
    SAE 74.81 85.27 66.45
    MJL-Model 70.92 85.96 62.87
    下载: 导出CSV

    表  3  消融实验结果

    Table  3  Results of ablation experiments

    ModelAns_F1Sup_F1Joint_F1
    MJL-Model78.8375.5162.72
    Question_answering76.36
    Answer_evidence73.42
    –MHSA 76.28 75.11 61.16
    –RCNN 75.96 75.05 60.96
    –Syntax&Similarity 77.61 74.39 60.80
    下载: 导出CSV
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