DMON: A Simple yet Effective Approach for Argument Structure Learning

• URL: http://arxiv.org/abs/2405.01216v1
• Date: Thu, 2 May 2024 11:56:16 GMT
• Title: DMON: A Simple yet Effective Approach for Argument Structure Learning
• Authors: Wei Sun, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens,
• Abstract summary: Argument structure learning (ASL) entails predicting relations between arguments. Despite its broad utilization, ASL remains a challenging task because it involves examining the complex relationships between the sentences in a potentially unstructured discourse. We have developed a simple yet effective approach called Dual-tower Multi-scale cOnvolution neural Network(DMON) for the ASL task.
• Score: 33.96187185638286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Argument structure learning~(ASL) entails predicting relations between arguments. Because it can structure a document to facilitate its understanding, it has been widely applied in many fields~(medical, commercial, and scientific domains). Despite its broad utilization, ASL remains a challenging task because it involves examining the complex relationships between the sentences in a potentially unstructured discourse. To resolve this problem, we have developed a simple yet effective approach called Dual-tower Multi-scale cOnvolution neural Network~(DMON) for the ASL task. Specifically, we organize arguments into a relationship matrix that together with the argument embeddings forms a relationship tensor and design a mechanism to capture relations with contextual arguments. Experimental results on three different-domain argument mining datasets demonstrate that our framework outperforms state-of-the-art models. The code is available at https://github.com/VRCMF/DMON.git .

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