TDGCN:触发器增强的两阶段动态图卷积网络的对话关系抽取研究  

TDGCN:Research on Conversation Relationship Extraction of Two Stage Dynamic Graph Convolutional Networks Enhanced by Triggers

作　　者：自彦丞 李卫疆[2] ZI Yancheng;LI Weijiang(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650500 [2]昆明理工大学云南省人工智能重点实验室,昆明650500

出　　处：《小型微型计算机系统》2025年第1期90-96,共7页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(62066022)资助。

摘　　要：随着互联网中对话数据的不断增加,从中提取关系三元组对于自然语言处理的各个下游任务至关重要.为了改进对话关系抽取的性能,D.Yu等人在数据集中引入了“触发器”的概念,该概念为关系抽取提供了重要的线索.然而,目前对于触发器的应用还仅仅限于将其作为一个模型训练的附加任务,并未在关系三元组推理中充分利用.本文提出了一个两阶段的动态图模型,通过引入动态机制,有效地改进了现有静态构造的图注意力模型在处理关系重叠时的歧义问题.并且在动态图模型中引入了触发器节点,以便更充分地利用触发器来进行关系推理.整个模型在DialogRE数据集上进行了实验,相对于基线模型,该模型在验证集上的F1值提升了2.2%,在测试集上提升了2%.并且本文对所提出的机制进行了进一步分析,通过实验验证了其有效性.With the continuous increase of conversation data in the Internet,extracting relational triples from it is crucial for various downstream tasks of natural language processing.In order to improve the performance of dialogue relationship extraction,D.Yu et al.introduced the concept of“triggers”in the dataset,which provides important clues for relationship extraction.However,the current application of triggers is only limited to using them as an additional task for model training,and has not been fully utilized in relational triplet reasoning.This article proposes a two-stage dynamic graph model,which effectively improves the ambiguity problem of existing statically constructed graph attention models when dealing with overlapping relationships by introducing dynamic mechanisms.And trigger nodes are introduced in the dynamic graph model to more fully utilize triggers for relational reasoning.The entire model was tested on the DialogRE dataset,and compared to the baseline model,the F1 value of the model increased by 2.2%on the validation set and 2%on the test set.And this article further analyzes the proposed mechanism and verifies its effectiveness through experiments.

关 键 词：动态图注意力网络 对话关系抽取 触发器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]