Adaptive Prototypical Networks With Label Words and Joint Representation Learning for Few-Shot Relation...

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Relation classification (RC) is crucial for information extraction, converting unstructured text into structured entity-relation triples.
• Distant supervision aids RC but introduces noise and struggles with long-tail data distributions.
• Few-shot relation classification (FSRC) aims to generalize classifiers to new classes with limited data, mimicking human learning.

Purpose of the Study:

• To enhance feature representation for few-shot relation classification by adaptively encoding class prototypes.
• To address the limitations of existing methods in handling limited data and improving generalization in FSRC.

Main Methods:

• Proposing an adaptive mixture mechanism to incorporate label words into class prototype representations.
• Introducing a joint representation learning (JRL) loss function for adaptive encoding of support instances.
• Utilizing prototypical networks as a foundation for adaptive prototype encoding.

Main Results:

• The proposed adaptive prototypical networks significantly improved accuracy in few-shot relation classification tasks.
• The method demonstrated enhanced generalization ability for FSRC on the FewRel dataset.
• Integrating label words and JRL led to more interactive class prototypes and better distance measurements.

Conclusions:

• Adaptive prototypical networks with label words and JRL offer a robust solution for few-shot relation classification.
• The approach effectively leverages limited data to improve model performance and generalization.
• This work represents a novel integration of label information into prototype features for FSRC.