Document-level Relation Extraction as Semantic Segmentation (2106.03618v2)

Abstract: Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information, parallel to the semantic segmentation task in computer vision. Herein, we propose a Document U-shaped Network for document-level relation extraction. Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples. Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets DocRED, CDR, and GDA.

Semantic Segmentation for Document-Level Relation Extraction: An Evaluation of DocuNet

The academic paper under review presents an exploration of the alignment between semantic segmentation techniques and document-level relation extraction (RE) tasks. It proposes a novel approach employing the DocuNet model, which integrates semantic segmentation methodologies—particularly U-Net—to enhance performance in document-level RE by treating relations between entities analogous to pixel classification.

Methodological Innovation

The authors identify three principal observations that underscore the synergy between semantic segmentation and document-level RE:

1. Entity-Pair Representation: Semantic segmentation traditionally labels image pixels to form comprehensive classes. Similarly, the DocuNet model formulates an entity-level relational matrix, analogous to semantic segmentation pixels, to compute entity-to-entity relevance. This involves employing similarity-based and context-based methods within the encoder module to encapsulate and employ contextual information effectively.
2. Local Semantic Dependencies: The task of RE benefits significantly from the convolutional neural network (CNN) dynamics present in semantic segmentation, facilitating local exchange of information between entity pairs. This is vital for identifying relations within closely associated entities in the text.
3. Global Information Integration: The paper elucidates the significance of global contextual information in deciphering relations among triples. Techniques such as down-sampling and up-sampling blocks, akin to those in semantic segmentation models, serve to broaden the receptive field of entity pair embeddings, thereby enhancing implicit reasoning capabilities for global relation extraction.

Key Contributions

The paper emphasizes several contributions of the DocuNet model across different dimensions:

• Engineering: Provides a pragmatic implementation of integrating U-Net into document-level RE, demonstrating applicability in enhancing entity relation mapping.
• Educational Insight: Through empirical evaluation, the model elaborates on the crucial role of local context and global interdependencies in relation extraction tasks.
• Theoretical Framework: Offers a theoretical foundation for utilizing convolutional operations to model interactions between triples in document-level RE.

Practical and Theoretical Implications

The integration of semantic segmentation in document-level RE proposes practical implications for enhancing entity-relation mapping accuracy. The use of balanced softmax loss addresses common challenges such as imbalanced relation distributions, often faced in natural document-level data settings. Furthermore, theorizing convolution operations within RE delineates pathways for future AI models to potentially streamline and optimize complex relation computations.

Speculation on Future Developments

In terms of future developments, the paper suggests potential for extension beyond U-Net through application of newer, albeit more complex, semantic segmentation models to further optimize document-level RE performance. The model serves as a basis for continued exploration of convolution-based approaches within RE tasks, possibly offering adaptability across various natural language processing applications requiring sophisticated entity-relation mapping techniques.

In summary, while the DocuNet model introduces a substantive proof-of-concept for applying semantic segmentation techniques to document-level RE, further research is warranted to explore extended methodologies and refine capabilities across varied datasets and linguistic contexts.