DMON: A Simple yet Effective Approach for Argument Structure Learning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909188048814080 |
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| author | Sun, Wei Li, Mingxiao Sun, Jingyuan Davis, Jesse Moens, Marie-Francine |
| author_facet | Sun, Wei Li, Mingxiao Sun, Jingyuan Davis, Jesse Moens, Marie-Francine |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DMON: A Simple yet Effective Approach for Argument Structure Learning Sun, Wei Li, Mingxiao Sun, Jingyuan Davis, Jesse Moens, Marie-Francine Computation and Language Artificial Intelligence 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 . |
| title | DMON: A Simple yet Effective Approach for Argument Structure Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.01216 |