Adaptive class augmented prototype network for few-shot relation extraction.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Relation extraction is crucial for knowledge construction but requires extensive annotated data.
• Few-shot relation extraction addresses data scarcity by learning from limited examples.
• Existing methods struggle with intra-class/inter-class discrepancies and biased features.

Purpose of the Study:

• To introduce an adaptive class augmented prototype network for few-shot relation extraction.
• To enhance representation learning by strengthening instance-level and representation-level features.
• To improve model generalization and accuracy on unseen relation classes.

Main Methods:

• Developed an adaptive class augmentation mechanism for instance-level expansion.
• Implemented class augmented representation learning with Bernoulli perturbation context attention.
• Employed adaptive debiased contrastive learning for model training.

Main Results:

• The proposed network significantly improved accuracy and generalization in few-shot settings.
• Enhanced performance was observed, particularly in cross-domain and challenging tasks.
• The model effectively addressed issues of biased class features and spurious correlations.

Conclusions:

• The adaptive class augmented prototype network offers a robust solution for few-shot relation extraction.
• The augmentation and debiasing techniques strengthen representation learning.
• This approach advances the field by improving model adaptability and performance on limited data.