基于异质图卷积神经网络的论点对抽取模型

doi:10.3785/j.issn.1008-973X.2024.05.003

浙江大学学报(工学版)

2024, Vol. 58

Issue (5): 900-907 DOI: 10.3785/j.issn.1008-973X.2024.05.003

计算机技术、通信技术

基于异质图卷积神经网络的论点对抽取模型

刘议丹(

),朱小飞*(

),尹雅博

重庆理工大学计算机科学与工程学院，重庆 400054

Heterogeneous graph convolutional neural network for argument pair extraction

Yidan LIU(

),Xiaofei ZHU*(

),Yabo YIN

College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China

全文: PDF(850 KB) HTML

摘要：

针对论点对抽取任务中存在着评审段和反驳段之间交互信息难以捕获以及忽略了对句子间的相对位置信息进行建模问题, 提出基于异质图卷积神经网络的论点对抽取模型. 该模型在评审段和反驳段中构建异质图，定义2种不同类型的节点及4种不同类型的边，通过关系图卷积神经网络来更新图中节点的表示. 提出位置感知的句子对生成器，利用旋转位置编码来建模评审段和反驳段句子间的相对位置信息. 在RR-passage和RR-submission-v2数据集上进行实验，实验结果表明，提出模型的性能均优于所有的基线模型. 这表明通过构建异质图区分不同的节点类型和边的类型，设计位置感知的句子对生成器，能够提升论点对抽取模型的效果.

关键词： 论辩挖掘; 论点对抽取; 图神经网络; 旋转位置编码; 自然语言处理

Abstract:

An argument pair extraction model based on heterogeneous graph convolutional neural network was proposed aiming at the issue of difficulty in capturing interactive information between review passage and rebuttal passage and neglecting to model relative positional information between sentences. Heterogeneous graphs were constructed within the review passage and rebuttal passage. Two types of nodes and four types of edges were defined. The relational graph convolutional neural network was utilized to update the representations of nodes within the graph. A position-aware sentence pair generator was introduced, and rotary position embedding was employed to model the relative positional information between sentences in review passage and rebuttal passage. Experimental evaluations on the RR-passage and RR-submission-v2 datasets demonstrate that the proposed model outperforms all baseline models. The performance of the argument pair extraction model can be enhanced by constructing heterogeneous graphs to distinguish between different types of nodes and edges and designing a position-aware sentence pair generator.

Key words: argument mining argument pair extraction graph neural network rotary position embedding natural language processing

收稿日期: 2023-07-03 出版日期: 2024-04-26

CLC:

TP 393

基金资助: 国家自然科学基金资助项目（62141201）；重庆市自然科学基金资助项目 (CSTB2022NSCQ-MSX1672)；重庆市教育委员会科学技术研究计划资助项目(KJZD-M202201102)；重庆理工大学研究生教育高质量发展行动计划资助项目(gzlcx20233230).

通讯作者: 朱小飞 E-mail: lyd@stu.cqut.edu.cn;zxf@cqut.edu.cn

作者简介: 刘议丹（1999—），男，硕士生，从事论辩挖掘的研究. orcid.org/0009-0005-1101-8294.E-mail：lyd@stu.cqut.edu.cn

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引用本文:

刘议丹,朱小飞,尹雅博. 基于异质图卷积神经网络的论点对抽取模型[J]. 浙江大学学报(工学版), 2024, 58(5): 900-907.

Yidan LIU,Xiaofei ZHU,Yabo YIN. Heterogeneous graph convolutional neural network for argument pair extraction. Journal of ZheJiang University (Engineering Science), 2024, 58(5): 900-907.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.05.003 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I5/900

图 1 HGCN-APE模型架构

图 2 HGCN-APE异质图构建

图 3 HGCN-APE超参数$\lambda $的影响

表 1 论点对抽取数据集统计

表 2 HGCN-APE在RR-passage和RR-submission-v2数据集上的性能对比

表 3 HGCN-APE在RR-passage数据集上的消融实验

表 4 HGCN-APE超参数$L$的影响

图 4 HGCN-APE在RR-passage和RR-submission-v2数据集上的损失和性能对比

表 5 HGCN-APE案例研究

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