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 .

翻译：论元结构学习（ASL）旨在预测论元之间的关系。由于它能够结构化文档以促进理解，该方法已广泛应用于医疗、商业和科学等多个领域。尽管应用广泛，ASL仍然是一项具有挑战性的任务，因为它需要分析潜在非结构化语篇中句子间的复杂关系。为解决这一问题，我们提出了一种名为双塔多尺度卷积神经网络（DMON）的简单有效方法。具体而言，我们将论元组织成关系矩阵，与论元嵌入共同构成关系张量，并设计了一种机制来捕获论元与上下文论元之间的关系。在三个不同领域的论元挖掘数据集上的实验结果表明，我们的框架优于现有最佳模型。代码地址为：https://github.com/VRCMF/DMON.git。