Sentence-Level Relation Semantics Learning via Contrastive Sentences

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Sentence-level semantics are crucial for language understanding.
• Existing relational triple extraction (RTE) models often overlook sentence semantics and inter-sample dependencies.
• This leads to relation-unaware semantics and individually trained samples, hindering performance.

Purpose of the Study:

• To address limitations in current RTE models.
• To incorporate relation information into text encoding for relation-aware semantics.
• To leverage inter-sample dependencies for learning discriminative semantics.

Main Methods:

• Proposed a model-agnostic multi-relation detection task.
• Developed model-agnostic multi-relation supervised contrastive learning.
• Designed reverse label frequency weighting and hierarchical label embedding mechanisms.

Main Results:

• The proposed method significantly and consistently improved performance across various backbones.
• Experiments were conducted on four public benchmarks.
• Model analysis confirmed the effectiveness of the proposed techniques.

Conclusions:

• The novel approach enhances RTE models by integrating relation-aware semantics and inter-sample dependencies.
• The method offers a flexible and effective way to improve relational triple extraction.
• The techniques address challenges like label imbalance and relation hierarchy.