Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the domain, entities, and relations. In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning. While representations in relation classification are commonly extracted using entity marker tokens, we argue that substantial information within the internal model representations remains untapped. To address this, we propose aligning multiple sentence representations, such as the [CLS] token, the [MASK] token used in prompting, and entity marker tokens. Our method employs contrastive learning to extract complementary discriminative information from these individual representations. This is particularly relevant in low-resource settings where information is scarce. Leveraging multiple sentence representations is especially effective in distilling discriminative information for relation classification when additional information, like relation descriptions, are not available. We validate the adaptability of our approach, maintaining robust performance in scenarios that include relation descriptions, and showcasing its flexibility to adapt to different resource constraints.

翻译：在少样本关系分类中，利用有限的标注实例区分实体对之间的关系是一项重大挑战。文本数据的表示能提取涵盖领域、实体和关系的丰富信息。本文提出一种结合多重句子表示与对比学习的新型信息增强提取方法。尽管关系分类中普遍采用实体标记令牌提取表示，但模型内部表示中仍存在大量未开发的信息。为此，我们提出对齐多重句子表示，包括[CLS]令牌、提示机制中使用的[MASK]令牌以及实体标记令牌。我们的方法通过对比学习从这些独立表示中提取互补的判别性信息，这在信息稀缺的低资源场景中尤为重要。当缺少关系描述等额外信息时，利用多重句子表示能更有效地提炼出关系分类所需的判别性信息。我们验证了该方法的适应性，在包含关系描述的场景中仍保持稳定性能，展示了其适应不同资源约束的灵活性。