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 this https URL .
Abstract (translated)
翻译：论据结构学习~（ASL）意味着预测论据之间的关系。由于它可以组织文档以促进其理解，因此在医学、商业和科学等领域得到了广泛应用。（ASL在许多领域中都有广泛应用，包括医学、商业和科学领域。）尽管ASL的应用非常广泛，但它仍然是一个具有挑战性的任务，因为它涉及检查潜在无结构话语中句子的复杂关系。为了解决这个问题，我们开发了一种简单而有效的ASL任务解决方案：双塔多尺度卷积神经网络~（DMON）。具体来说，我们将论据组织成一个关系矩阵，该矩阵与论据嵌入一起形成关系张量，并设计了一个机制来捕捉带有上下文论据的关系。在三个不同领域的论据挖掘数据集上的实验结果表明，我们的框架超过了最先进的模型。代码可在此处访问：https://www.jianshu.com/p/142637911311920001 。
URL
https://arxiv.org/abs/2405.01216
