Transformer Enhanced Relation Classification: A Comparative Analysis of Contextuality, Data Efficiency and Sequence Complexity

2025-09-14

AI Summary

This study systematically investigates performance disparities between Transformer-based and non-Transformer models in relation classification, with emphasis on contextual modeling capability, few-shot learning efficiency, and robustness to long sequences. Method: We conduct comprehensive experiments across three benchmark datasets—TACRED, TACREV, and RE-TACRED—comparing representative Transformer architectures (BERT, RoBERTa, R-BERT) against prominent non-Transformer models (PA-LSTM, C-GCN, AGGCN). Crucially, we perform the first cross-architectural analysis across multiple dimensions: data scale, sentence length, and annotation density. Contribution/Results: Our results reveal a structural advantage of Transformers in capturing long-range contextual dependencies and generalizing across settings: they achieve micro-F1 scores of 80–90%, substantially outperforming non-Transformer counterparts (64–67%). Gains are especially pronounced under low-resource conditions and for long sentences. These findings provide empirical guidance for model selection and architectural design in relation classification.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In the era of large language model, relation extraction (RE) plays an important role in information extraction through the transformation of unstructured raw text into structured data (Wadhwa et al., 2023). In this paper, we systematically compare the performance of deep supervised learning approaches without transformers and those with transformers. We used a series of non-transformer architectures such as PA-LSTM(Zhang et al., 2017), C-GCN(Zhang et al., 2018), and AGGCN(attention guide GCN)(Guo et al., 2019), and a series of transformer architectures such as BERT, RoBERTa, and R-BERT(Wu and He, 2019). Our comparison included traditional metrics like micro F1, as well as evaluations in different scenarios, varying sentence lengths, and different percentages of the dataset for training. Our experiments were conducted on TACRED, TACREV, and RE-TACRED. The results show that transformer-based models outperform non-transformer models, achieving micro F1 scores of 80-90% compared to 64-67% for non-transformer models. Additionally, we briefly review the research journey in supervised relation classification and discuss the role and current status of large language models (LLMs) in relation extraction.

Problem

• Comparing transformer and non-transformer models for relation extraction
• Evaluating performance across different data efficiency scenarios
• Analyzing model effectiveness with varying sentence complexity

Innovation

• Transformer-based models outperform non-transformer architectures
• Systematic comparison across multiple evaluation scenarios
• Achieved 80-90% F1 scores using BERT variants