基于监督对比重放的持续关系抽取

Continual Relation Extraction via Supervised Contrastive Replay

持续关系抽取被用来解决在新关系上重新训练模型而导致灾难性遗忘的问题。该文针对现有持续关系抽取模型存在的最近任务偏倚等问题,提出了一种基于监督对比重放的持续关系抽取方法。具体而言,对每个新任务,首先利用编码器学习新的样本嵌入,接着通过将相同和不同关系类别的样本作为正负样本对,在每次重放的过程中利用监督对比损失,不断学习一个区分能力强的编码器;同时,在监督对比学习过程中利用关系原型进行辅助增强,防止模型过拟合;最后在测试阶段通过最近类均值分类器进行分类。实验结果表明,该文提出的方法可以有效缓解持续关系抽取中的灾难性遗忘问题,在FewRel和TACRED两个数据集上都达到了最先进的持续关系抽取性能。同时,随着任务数量的增加,在训练至5个任务以后,该文模型性能领先最先进的模型性能约1%。

Continual relation extraction is used to solve catastrophic forgetting caused by retraining models on new relations.Aiming at task-recency bias issue,this paper proposes a continual relation extraction method based on supervised contrastive replay.Specifically,for each new task,the model first uses the encoder to learn new sample embeddings,and then uses the samples of the same and different relation categories as positive and negative sample pairs to continually learn an embedding space with strong discrimination ability.At the same time,relation prototypes are added to the supervised contrastive loss to prevent the model from overfitting.Finally,the nearest class mean classifier is used for classification.The experimental results show that the proposed method can effectively alleviate the catastrophic forgetting issue in continual relation extraction,and achieve the state-of-the-art performance on FewRel and TACRED datasets.