Aspect-Based Argument Mining (ABAM) is a critical task in computational argumentation. Existing methods have primarily treated ABAM as a nested named entity recognition problem, overlooking the need for tailored strategies to effectively address the specific challenges of ABAM tasks. To this end, we propose a layer-based Hierarchical Enhancement Framework (HEF) for ABAM, and introduce three novel components: the Semantic and Syntactic Fusion (SSF) component, the Batch-level Heterogeneous Graph Attention Network (BHGAT) component, and the Span Mask Interactive Attention (SMIA) component. These components serve the purposes of optimizing underlying representations, detecting argument unit stances, and constraining aspect term recognition boundaries, respectively. By incorporating these components, our framework enables better handling of the challenges and improves the performance and accuracy in argument unit and aspect term recognition. Experiments on multiple datasets and various tasks verify the effectiveness of the proposed framework and components.

“随着社交媒体的快速发展,多模态数据呈爆炸性增长,如何从多模态数据中挖掘和理解情感信息,已经成为一个较为热门的研究方向。而现有的基于文本、视频和音频的多模态情感分析方法往往将不同模态的高级特征与低级特征进行融合,忽视了不同模态特征层次之间的差异。因此,本文采用以文本模态为中心,音频模态和视频模态为补充的方式,提出了多任务多模态交互学习的自监督动态融合模型。通过多层的结构,构建了单模态特征表示与两两模态特征的层次融合表示,使模型将不同层次的特征进行融合,并设计了从低级特征渐变到高级特征的融合策略。为了进一步加强多模态特征融合,使用了分布相似性损失函数和异质损失函数,用于学习模态的共性表征和特性表征。在此基础上,利用多任务学习,获得模态的一致性及差异性特征。通过在CMU-MOSI和CMU-MOSEI数据集上分别实验,实验结果表明本文模型的情感分类性能优于基线模型。”