Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Many few-shot models have been widely used for relation learning tasks. However, each of these models has a shortage of capturing a certain aspect of semantic features, for example, CNN on long-range dependencies part, Transformer on local features. It is difficult for a single model to adapt to various relation learning, which results in the high variance problem. Ensemble strategy could be competitive on improving the accuracy of few-shot relation extraction and mitigating high variance risks. This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features. Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.

翻译：作为完成各种任务的可行手段,提出了少见的学习建议,并迅速出现,许多少见的模型被广泛用于关系学习任务,但是,每个模型都缺乏掌握语义特征的某些方面,例如,有线电视新闻网关于远距离依赖的部分,变异器关于当地特点的部分,很难采用单一模型来适应各种关系学习,从而导致差异很大的问题。综合战略在提高少见关系提取的准确性和减少高差异风险方面可能具有竞争力。本文探讨了减少差异的共同方法,并引入调整关系层面特征的微调和突出关注战略。一些短视关系学习任务的结果显示,我们的模式大大超越了以前的最先进的模式。