  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 .
