Multi-Task Sequence Tagging for Denoised Causal Relation Extraction

Yijia Zhang, Chaofan Liu, Yuan Zhu and Wanyu Chen ()
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Yijia Zhang: College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China
Chaofan Liu: College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China
Yuan Zhu: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Wanyu Chen: College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: Extracting causal relations from natural language texts is crucial for uncovering causality, and most existing causal relation extraction models are single-task learning-based models, which can not comprehensively address attributes such as part-of-speech tagging and chunk analysis. However, the characteristics of words with multi-domains are more relevant for causal relation extraction, due to words such as adjectives, linking verbs, etc., bringing more noise data limiting the effectiveness of the single-task-based learning methods. Furthermore, causalities from diverse domains also raise a challenge, as existing models tend to falter in multiple domains compared to a single one. In light of this, we propose a multi-task sequence tagging model, MPC−CE, which utilizes more information about causality and relevant tasks to improve causal relation extraction in noised data. By modeling auxiliary tasks, MPC−CE promotes a hierarchical understanding of linguistic structure and semantic roles, filtering noise and isolating salient entities. Furthermore, the sparse sharing paradigm extracts only the most broadly beneficial parameters by pruning redundant ones during training, enhancing model generalization. The empirical results on two datasets show 2.19% and 3.12% F1 improvement, respectively, compared to baselines, demonstrating that our proposed model can effectively enhance causal relation extraction with semantic features across multiple syntactic tasks, offering the representational power to overcome pervasive noise and cross-domain issues.

Keywords: causal relation extraction; sequence tagging; multi-task learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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