标题： CPTuning：生成式关系抽取的对比提示微调 显示英文标题

标题： CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction

摘要： 生成式关系抽取（RE）通常首先将RE重新表述为一个易于通过预训练语言模型（PLM）解决的语言建模问题，然后使用监督交叉熵损失对PLM进行微调。 尽管取得了有希望的性能，现有方法假设每对实体之间只有一种确定性关系，而没有考虑实际场景中可能存在多种有效关系的情况，即实体对重叠，导致其应用受限。 为了解决这个问题，我们引入了一种新颖的对比提示微调方法用于RE，称为CPTuning，它学习将两个上下文实体之间的候选关系与高于或低于阈值的概率质量相关联，对应于该关系是否存在。 除了学习模式，CPTuning还将RE组织为一种显式关系生成任务，并使用Trie约束解码以确保模型生成有效关系。 它在推理中自适应地选择生成的候选关系，这些关系具有较高的估计可能性，从而实现了多关系抽取。 我们在四个广泛使用的数据集上进行了大量实验以验证我们的方法。 结果表明，使用CPTuning微调的T5-large显著优于之前的方法，无论是在单关系还是多关系抽取方面。 显示英文摘要

摘要： Generative relation extraction (RE) commonly involves first reformulating RE as a linguistic modeling problem easily tackled with pre-trained language models (PLM) and then fine-tuning a PLM with supervised cross-entropy loss. Although having achieved promising performance, existing approaches assume only one deterministic relation between each pair of entities without considering real scenarios where multiple relations may be valid, i.e., entity pair overlap, causing their limited applications. To address this problem, we introduce a novel contrastive prompt tuning method for RE, CPTuning, which learns to associate a candidate relation between two in-context entities with a probability mass above or below a threshold, corresponding to whether the relation exists. Beyond learning schema, CPTuning also organizes RE as a verbalized relation generation task and uses Trie-constrained decoding to ensure a model generates valid relations. It adaptively picks out the generated candidate relations with a high estimated likelihood in inference, thereby achieving multi-relation extraction. We conduct extensive experiments on four widely used datasets to validate our method. Results show that T5-large fine-tuned with CPTuning significantly outperforms previous methods, regardless of single or multiple relations extraction.