DMON: A Simple yet Effective Approach for Argument Structure Learning
<|reference_start|>DMON: A Simple yet Effective Approach for Argument Structure Learning: 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 .<|reference_end|>
@article{sun2024dmon:,
    title={DMON: A Simple yet Effective Approach for Argument Structure Learning},
    author={Wei Sun, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens},
    journal={arXiv preprint arXiv:2405.01216},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01216},
    primaryClass={cs.CL cs.AI}
}