While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives. In this paper, we introduce Hyper-CL, an efficient methodology that integrates hypernetworks with contrastive learning to compute conditioned sentence representations. In our proposed approach, the hypernetwork is responsible for transforming pre-computed condition embeddings into corresponding projection layers. This enables the same sentence embeddings to be projected differently according to various conditions. Evaluation on two representative conditioning benchmarks, namely conditional semantic text similarity and knowledge graph completion, demonstrates that Hyper-CL is effective in flexibly conditioning sentence representations, showcasing its computational efficiency at the same time. We also provide a comprehensive analysis of the inner workings of our approach, leading to a better interpretation of its mechanisms.

翻译：尽管对比学习框架在句子表示学习中的引入显著推动了该领域的发展，但当前最先进的句子嵌入能否捕捉句子的细粒度语义——尤其是在特定视角下进行调节时——仍不明确。本文提出Hyper-CL，一种将超网络与对比学习相结合的高效方法，用于计算条件化句子表示。在该方法中，超网络负责将预计算的条件嵌入转换为相应的投影层，从而使得相同的句子嵌入能够根据不同条件产生差异化的投影结果。在两个代表性调节基准任务（即条件语义文本相似度与知识图谱补全）上的评估表明，Hyper-CL能有效灵活地调节句子表示，同时展现出计算高效性。我们还对所提方法的内在机制进行了全面分析，从而更深入地阐释其运作原理。