Semantic-guided attention and adaptive gating for document-level relation extraction

Area of Science:

• Natural Language Processing (NLP)
• Artificial Intelligence (AI)

Background:

• Document-level relation extraction is challenging due to the complexity of capturing contextual interactions across sentences.
• Current graph- and transformer-based models struggle to fully exploit semantic information from multiple interactive sentences, leading to the exclusion of influential context.

Purpose of the Study:

• To develop a novel model, Semantic-guided Attention and Adaptively Gated (SAAG), for enhanced document-level relation extraction.
• To address the limitations of existing models in exploiting multi-sentence semantic information.

Main Methods:

• The SAAG model incorporates a semantic-guided attention module to assign differential importance to words, enhancing sentence representation.
• A multihead attention mechanism is employed to capture diverse semantic subspaces, generating a comprehensive document context representation.
• An adaptively gated mechanism dynamically distinguishes between local and global contexts to exploit semantic information effectively.

Main Results:

• The SAAG model demonstrated superior performance compared to previous state-of-the-art models.
• Experiments were conducted on two public datasets, validating the model's effectiveness.

Conclusions:

• The SAAG model offers a significant advancement in document-level relation extraction by effectively leveraging multi-sentence semantic context.
• The proposed attention and gating mechanisms are crucial for capturing nuanced relationships within unstructured documents.