论文标题
查看标签：带有TOP-K预测设置的标签图网络用于关系提取
Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
论文作者
论文摘要
关系提取的典型方法是在特定于任务的数据集上微调大型预训练的预训练的语言模型，然后选择具有输出分布最高概率的标签作为最终预测。但是，通常会忽略针对给定样本的TOP-K预测设置。在本文中，我们首先揭示了给定样本的顶级预测集包含用于预测正确标签的有用信息。为了有效利用TOP-K预测集，我们建议使用TOP-K预测集的标签图网络，称为KLG。具体来说，对于给定的样本，我们构建了一个标签图，以在Top-K预测集中查看候选标签并学习它们之间的连接。我们还设计了一种动态的$ k $选择机制，以学习更强大和歧视性的关系表示。我们的实验表明，KLG在三个关系提取数据集上取得了最佳性能。此外，我们观察到，KLG在处理长尾班时更有效。
The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic $k$-selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe that KLG is more effective in dealing with long-tailed classes.
