Adaptive class augmented prototype network for few-shot relation extraction.
Neural Netw
; 169: 134-142, 2024 Jan.
Article
en En
| MEDLINE
| ID: mdl-37890363
ABSTRACT
Relation extraction is one of the most essential tasks of knowledge construction, but it depends on a large amount of annotated data corpus. Few-shot relation extraction is proposed as a new paradigm, which is designed to learn new relationships between entities with merely a small number of annotated instances, effectively mitigating the cost of large-scale annotation and long-tail problems. To generalize to novel classes not included in the training set, existing approaches mainly focus on tuning pre-trained language models with relation instructions and developing class prototypes based on metric learning to extract relations. However, the learned representations are extremely sensitive to discrepancies in intra-class and inter-class relationships and hard to adaptively classify the relations due to biased class features and spurious correlations, such as similar relation classes having closer inter-class prototype representation. In this paper, we introduce an adaptive class augmented prototype network with instance-level and representation-level augmented mechanisms to strengthen the representation space. Specifically, we design the adaptive class augmentation mechanism to expand the representation of classes in instance-level augmentation, and class augmented representation learning with Bernoulli perturbation context attention to enhance the representation of class features in representation-level augmentation and explore adaptive debiased contrastive learning to train the model. Experimental results have been demonstrated on FewRel and NYT-25 under various few-shot settings, and the proposed model has improved accuracy and generalization, especially for cross-domain and different hard tasks.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Generalización Psicológica
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Idioma:
En
Revista:
Neural Netw
Asunto de la revista:
NEUROLOGIA
Año:
2024
Tipo del documento:
Article