Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2010.03851v1)

Abstract: Named entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task as a table-filling problem. However, they typically focused on learning a single encoder (usually learning representation in the form of a table) to capture information required for both tasks within the same space. We argue that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process. In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process. Our experiments confirm the advantages of having {\em two} encoders over {\em one} encoder. On several standard datasets, our model shows significant improvements over existing approaches.

Overview of Joint Entity and Relation Extraction with Table-Sequence Encoders

The paper introduces a novel model architecture termed as "table-sequence encoders" for joint named entity recognition (NER) and relation extraction (RE). Rather than utilizing a single encoder to handle both tasks, the proposed paradigm employs two distinct, yet interlinked, encoders: a table encoder and a sequence encoder. This division aims to mitigate feature confusion observed in traditional single-encoder setups, promoting task-specific representation learning.

Methodological Innovation

The key innovation lies in the dual-encoder framework, allowing bidirectional interactions between sequence and table representations. The sequence encoder builds on Transformer architecture, replacing traditional self-attention mechanisms with a customized table-guided attention. This mechanism leverages representations from the table encoder, enhancing the detection and classification of word sequences with contextual awareness derived from the table structure.

The table encoder itself is designed using Multi-Dimensional Recurrent Neural Networks (MD-RNN). This enables comprehensive context modeling within a table structure by exploiting layer-wise, row-wise, and column-wise information. The MD-RNN is particularly adept at capturing structural dependencies that are crucial for accurately identifying relationships between entities in text.

Empirical Evaluation

The paper conducts an exhaustive evaluation across four standard datasets: ACE04, ACE05, CoNLL04, and ADE, comparing the proposed system with leading models. Across all datasets, the table-sequence encoders achieve superior results for both NER and RE tasks, notable improvements being observed over existing table filling and joint extraction methods.

The architecture particularly manifests strong performance gains in relation extraction. For instance, on the ACE05 dataset, the system outperforms previous methods with a substantial margin, achieving relation extraction F1 scores elevated by over 4 percentage points. Such results underline the efficacy of the proposed interaction between sequence and table representations in enhancing the quality of relation detection.

Theoretical and Practical Implications

The bifurcation of encoders aligns with the natural delineation between named entities and their semantic relationships. By reinforcing task-specific learning while maintaining inter-task collaboration, the proposed model can more effectively disentangle the complexities inherent in joint NER and RE. This separation further allows the tailoring of encoder parameters to the unique contextual needs of each task, potentially improving generalization.

From a practical standpoint, the enhanced extraction capabilities bear significant implications for information extraction in domains such as biomedical text mining and automated content analysis in multimedia databases. The integration with pre-trained LLMs like BERT or ALBERT, further bolstered by leveraging their attention weights, offers a promising avenue for fine-tuning and adapting the encoders across diverse tasks and languages.

Future Directions

Prospective research could explore extending the table-sequence interaction model to other structured prediction tasks in NLP. Applying similar architectures to document-level relation extraction or cross-lingual extraction tasks may yield insights into the adaptability and scalability of the approach. Moreover, integrating more sophisticated attention mechanisms or incorporating domain-specific knowledge bases could further refine the model's performance and applicability.

In summary, the proposed table-sequence encoder architecture offers a robust framework for joint entity and relation extraction, achieving notable gains in performance and providing a foundation for ongoing advancements in complex information extraction tasks.