Hierarchical Enhancement Framework for Aspect-based Argument Mining

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Hierarchical Enhancement Framework for Aspect-based Argument Mining
Title:
Hierarchical Enhancement Framework for Aspect-based Argument Mining
Journal Title:
The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)
DOI:
Keywords:
Publication Date:
06 December 2023
Citation:
Yujie Fu, Suge Wang, Xiaoli Li, Yang Li, Deyu Li, Jian Liao, JianXing Zheng, Hierarchical Enhancement Framework for Aspect-based Argument Mining, EMNLP 2023.
Abstract:
Aspect-Based Argument Mining (ABAM) is a critical task in computational argumentation that aims extract the argument units expressing support or opposing stances and the aspect terms mentioned within these argument units, from a given text discussing a controversial topic. However, existing approaches have primarily treated ABAM as a nested named entity recognition problem, overlooking the need for tailored strategies to effectively address the specific challenges and requirements of ABAM tasks. In this paper, we propose a layer-based Hierarchical Reinforcement Framework (HRF) for ABAM, which consists of four modules: basic module, argument unit enhancement module, aspect term enhancement module and decision module. In particular, within the basic module, we propose a Semantic and Syntactic Fusion (SSF) component, which uses graph attention network to compensate for the limitation of long short term memory network in forgetting long distance information, thereby constructing a robust underlying representation. Furthermore, the argument unit enhancement module introduces a Batch-level Heterogeneous Graph Attention Network (BHGAT) component, which facilitates communication and learning of opinion expressions among different argument units discussing the same topic. This component enhances the model’s ability to capture nuanced relationships and distinctions between argument units. To address the specific challenges related to aspect term recognition, the paper presents the Span Mask Interactive Attention (SMIA) component. This component effectively constrains the recognition range of aspect terms by leveraging span masks and interactive attention mechanisms. By doing so, it enables more precise identification and extraction of aspect terms within their specified boundaries. Finally, the decision module consolidates the results from the preceding modules to make the final decision regarding the extracted argument units and aspect terms. The effectiveness of the proposed framework and its individual components is verified through extensive experiments conducted on multiple datasets and various tasks, reaffirming their potential and value in the field of ABAM.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: I2R CORE FUNDING
Grant Reference no. : Nil

The works described in this paper are supported by the National Key Research and Development Program of China (2022QY0300-01), National Natural Science Foundation of China (62106130, 62076158, 62072294, 62272286), Natural Science Foundation of Shanxi Province, China (20210302124084), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi, China (2021L284), and CCF-Zhipu AI Large Model Foundation of China (CCF-Zhipu202310).
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