Hierarchical Enhancement Framework for Aspect-based Argument Mining (2023)

Abstract: 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.

## introduction argument mining, a critical task within computational argumentation, has gained considerable attention, evident from available datasets @xcite , emerging tasks @xcite , and machine learning models associated to this domain @xcite . as aspect-level sentiment analysis tasks have flourished, aspect-based argument mining (abam) has recognized the need to decompose argument units into smaller attributes and define aspect terms as components with specific meanings in arguments @xcite . ## framework this paper proposes a hef for aspect-based argument mining task, which consists of four modules: basic module, argument unit enhancement module, aspect term enhancement module and decision module. the architecture of the hef is visually depicted in figure 2. ## conclusion this paper presents a novel layer-based approach for the aspect-based argument mining task, utiliz-ing a hierarchical enhancement framework consisting of four modules: basic module, argument unit enhancement module, aspect term enhancement module, and decision module. the ssf component plays a crucial role in optimizing underlying representations, which can be utilized across various tasks. it enhances the framework's capability by incorporating syntactic information into the encoding process, improving performance on sequence labeling tasks. the bhgat component, effective for classification tasks involving topic information, enhances the framework's generalization capabilities. the smia component is specifically designed for aspect-based argument mining tasks, aiming to constrain the recognition range of aspect terms. it effectively improves the accuracy of aspect term recognition and contributes to the overall performance of the framework.