Lifelong Event Detection via Optimal Transport
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912069502107648 |
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| author | Dao, Viet Pham, Van-Cuong Tran, Quyen Le, Thanh-Thien Van, Linh Ngo Nguyen, Thien Huu |
| author_facet | Dao, Viet Pham, Van-Cuong Tran, Quyen Le, Thanh-Thien Van, Linh Ngo Nguyen, Thien Huu |
| contents | Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel approach, Lifelong Event Detection via Optimal Transport (LEDOT), that leverages optimal transport principles to align the optimization of our classification module with the intrinsic nature of each class, as defined by their pre-trained language modeling. Our method integrates replay sets, prototype latent representations, and an innovative Optimal Transport component. Extensive experiments on MAVEN and ACE datasets demonstrate LEDOT's superior performance, consistently outperforming state-of-the-art baselines. The results underscore LEDOT as a pioneering solution in continual event detection, offering a more effective and nuanced approach to addressing catastrophic forgetting in evolving environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08905 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lifelong Event Detection via Optimal Transport Dao, Viet Pham, Van-Cuong Tran, Quyen Le, Thanh-Thien Van, Linh Ngo Nguyen, Thien Huu Computation and Language Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel approach, Lifelong Event Detection via Optimal Transport (LEDOT), that leverages optimal transport principles to align the optimization of our classification module with the intrinsic nature of each class, as defined by their pre-trained language modeling. Our method integrates replay sets, prototype latent representations, and an innovative Optimal Transport component. Extensive experiments on MAVEN and ACE datasets demonstrate LEDOT's superior performance, consistently outperforming state-of-the-art baselines. The results underscore LEDOT as a pioneering solution in continual event detection, offering a more effective and nuanced approach to addressing catastrophic forgetting in evolving environments. |
| title | Lifelong Event Detection via Optimal Transport |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.08905 |