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.

本文介绍了一种基于序列化和 Transformer 结构的模型 TransAM，以捕捉三元组内部和跨三元组的实体交互，以解决 Few-shot 关系学习问题。在两个公共基准数据集 NELL-One 和 Wiki-One 上的实验表明了 TransAM 的有效性。

探索实体交互以进行小样本关系学习（学生摘要）

Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)

Existing continual relation learning (CRL) methods rely on plenty of labeled
training data for learning a new task, which can be hard to acquire in real
scenario as getting large and representative labeled data is often expensive
and time-consuming. It is therefore necessary for the model to learn novel
relational patterns with very few labeled data while avoiding catastrophic
forgetting of previous task knowledge. In this paper, we formulate this
challenging yet practical problem as continual few-shot relation learning
(CFRL). Based on the finding that learning for new emerging few-shot tasks
often results in feature distributions that are incompatible with previous
tasks' learned distributions, we propose a novel method based on embedding
space regularization and data augmentation. Our method generalizes to new
few-shot tasks and avoids catastrophic forgetting of previous tasks by
enforcing extra constraints on the relational embeddings and by adding extra
{relevant} data in a self-supervised manner. With extensive experiments we
demonstrate that our method can significantly outperform previous
state-of-the-art methods in CFRL task settings.

本文提出了一种基于嵌入空间规范化和数据增强的新模型，用于以少量标记数据学习新的关系模式，并避免对以前任务的重大遗忘，实验表明此方法在连续少样本关系学习任务中显著优于现有技术。