DMON: A Simple Yet Effective Approach for Argument Structure Learning

Sun Wei, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens

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. We will release the code after paper acceptance.
Anthology ID:
2024.lrec-main.455
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
5109–5118
Language:
URL:
https://aclanthology.org/2024.lrec-main.455
DOI:
Bibkey:
Cite (ACL):
Sun Wei, Mingxiao Li, Jingyuan Sun, Jesse Davis, and Marie-Francine Moens. 2024. DMON: A Simple Yet Effective Approach for Argument Structure Learning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5109–5118, Torino, Italia. ELRA and ICCL.
Cite (Informal):
DMON: A Simple Yet Effective Approach for Argument Structure Learning (Wei et al., LREC-COLING 2024)
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