对比了带与不带Transformer的抽取模型，发现前者在长文本和小数据下更优。
Transformer Enhanced Relation Classification: A Comparative Analysis of Contextuality, Data Efficiency and Sequence Complexity
- 用非Transformer和Transformer模型对比，评估不同长度句子和训练比例下的表现
- Transformer模型微F1达80-90%，非Transformer为64-67%，差距显著
- 适合关注关系抽取中模型选择与数据效率的研究者
在大语言模型时代，关系抽取（RE）通过将非结构化文本转化为结构化数据，在信息提取中发挥关键作用。本文系统比较了无Transformer与含Transformer的深度监督学习方法。采用多种非Transformer架构（PA-LSTM、C-GCN、AGGCN）和Transformer架构（BERT、RoBERTa、R-BERT）。实验在TACRED、TACREV和RE-TACRED数据集上进行，涵盖传统指标（如微F1）及不同句长、不同训练集占比下的表现。结果表明，基于Transformer的模型性能更优，微F1达到80-90%，而非Transformer模型仅为64-67%。同时简要回顾了监督式关系分类的发展历程，并讨论了大语言模型（LLMs）在关系抽取中的角色与现状。
原文摘要 · Abstract (English)
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.
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