# DMON: A Simple yet Effective Approach for Argument Structure Learning

arXiv: https://arxiv.org/abs/2405.01216 · HTML: https://mlsanity.com/paper/2405.01216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 20 of 20)

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- [ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer](https://mlsanity.com/paper/2203.03952/) (2022)
- [SciDTB: Discourse Dependency TreeBank for Scientific Abstracts](https://mlsanity.com/paper/1806.03653/) (2018)
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- [Convolution, attention and structure embedding](https://mlsanity.com/paper/1905.01289/) (2019)
- [Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus](https://mlsanity.com/paper/2305.11142/) (2023)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)
- [Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature](https://mlsanity.com/paper/2305.01361/) (2023)
- [Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis](https://mlsanity.com/paper/2201.02026/) (2022)

## Abstract (arXiv)

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 .