论文标题
探索实体互动以进行几次射击关系学习（学生摘要）
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
论文作者
论文摘要
很少有射击关系学习是指与有限观察到的三元组有限的关系推断事实。对于此问题的现有度量学习方法主要是忽略三元组内和之间的实体相互作用。在本文中，我们探讨了这种细粒的语义含义，并提出了我们的模型Transam。具体而言，我们将参考实体和查询实体序列化为顺序，并以局部全球关注的方式应用变压器结构以捕获内部和三级实体的相互作用。在两个公共基准数据集Nell-One和Wiki One的实验证明了Transam的有效性。
Few-shot relation learning refers to infer facts for relations with a limited number of observed triples. Existing metric-learning methods for this problem mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meanings and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture both intra- and inter-triple entity interactions. Experiments on two public benchmark datasets NELL-One and Wiki-One with 1-shot setting prove the effectiveness of TransAM.
