Abstract
Argument pair extraction (APE) is a fine-grained task of argument mining which aims to identify arguments offered by different participants in some discourse and detect interaction relationships between arguments from different participants. In recent years, many research efforts have been devoted to dealing with APE in a multi-task learning framework. Although these approaches have achieved encouraging results, they still face several challenging issues. First, different types of sentence relationships as well as different levels of information exchange among sentences are largely ignored. Second, they solely model interactions between argument pairs either in an explicit or implicit strategy, while neglecting the complementary effect of the two strategies. In this paper, we propose a novel Mutually Enhanced Multi-Scale Relation-Aware Graph Convolutional Network (MMR-GCN) for APE. Specifically, we first design a multi-scale relation-aware graph aggregation module to explicitly model the complex relationships between review and rebuttal passage sentences. In addition, we propose a mutually enhancement transformer module to implicitly and interactively enhance representations of review and rebuttal passage sentences. We experimentally validate MMR-GCN by comparing with the state-of-the-art APE methods. Experimental results show that it considerably outperforms all baseline methods, and the relative performance improvement of MMR-GCN over the best performing baseline MRC-APE in terms of F1 score reaches to 3.48% and 4.43% on the two benchmark datasets, respectively.
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RR-submission-v2 and RR-passage are open-source datasets and can be downloaded from https://github.com/ LiyingCheng95/ArgumentPairExtraction.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China [grant number 62141201]; the Natural Science Foundation of Chongqing, China [grant number CSTB2022NSCQ-MSX1672]; the Major Project of Science and Technology Research Program of Chongqing Education Commission of China [grant number KJZD-M202201102]; the Federal Ministry of Education and Research [grant number 01IS21086].
Funding
National Natural Science Foundation of China [grant number 62141201], Natural Science Foundation of Chongqing, China [grant number CSTB2022NSCQ-MSX1672], Major Project of Science and Technology Research Program of Chongqing Education Commission of China [grant number KJZD-M202201102], Federal Ministry of Education and Research [grant number 01IS21086].
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Xiaofei Zhu: Conceptualization, Methodology, Funding acquisition. Yidan Liu: Software, Methodology, Writing- Original draft preparation. Zhuo Chen: Writing- Reviewing and Editing. Xu Chen: Supervision, Funding acquisition. Jiafeng Guo: Supervision, Funding acquisition. Stefan Dietze: Writing- Reviewing and Editing.
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Zhu, X., Liu, Y., Chen, Z. et al. A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00826-9
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DOI: https://doi.org/10.1007/s10844-023-00826-9