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征学习中的引入显著推动了该领域的发展，但最先进的句子嵌入是否能够捕捉句子的细粒度语义（尤其是在特定视角下进行条件调节时）仍不明确。本文提出Hyper-CL，一种将超网络与对比学习相结合以计算条件句子表征的高效方法。在该方法中，超网络负责将预计算的条件嵌入转换为对应的投影层，从而使相同的句子嵌入能够根据不同条件进行差异化投影。在两个典型条件调节基准任务（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征，同时展现出计算效率。我们还对方法的内部机制进行了全面分析，从而更深入地解读其工作原理。