Bi-directional feature learning-based approach for zero-shot event argument extraction

Ji Zhou

doi:10.1016/j.ipm.2025.104199

Bi-directional feature learning-based approach for zero-shot event argument extraction

2025-06-06

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DOI：10.1016/j.ipm.2025.104199

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Recent research has shown that event argument extraction (EAE) methods based on transfer learning and data augmentation emphasize the contribution of contextual features and labeled features to zero-shot EAE tasks, respectively. However, these methods suffer from knowledge transfer insufficiency and context generation bias challenges. In this paper, we propose a bidirectional feature learning-based approach for zero-shot event argument extraction (BiTer), which gains bi-directional transferable knowledge and mitigates context generation bias. Specifically, BiTer contains source and target model training. During source model training, BiTer co-trains the contextual and labeled feature learning tasks on the source dataset. This step enables the target model to acquire bi-directional transferable knowledge, providing more appropriate feature representations for target events. In target model training, BiTer leverages the large language model to produce pseudo-arguments, and then the knowledge-embedded model generates training data of the target events based on them. This step mitigates context generation bias and makes BiTer learn a more comprehensive and precise feature of the target event. Extensive experiments on RAMS, WIKIEVENTS and ACE2005 have demonstrated BiTer achieves a new state-of-the-art level, with F1 in the zero-shot setting outperforming the baseline model by 4.6%, 7.5% and 0.4%, respectively.

Keywords:

Event argument extraction

Zero shot

Text generation

Feature learning

Bi-directional feature learning-based approach for zero-shot event argument extraction

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