Hyper-CL：利用超网络对句子表示进行条件化
Hyper-CL: Conditioning Sentence Representations with Hypernetworks
AI总结：
本文提出Hyper-CL方法，通过超网络将条件嵌入转换为投影层，并与对比学习结合，实现灵活高效的条件化句子表示，在条件语义文本相似度和知识图谱补全任务上验证了有效性。
摘要：
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，但目前仍不清楚最先进的句子嵌入是否能够捕捉句子的细粒度语义，尤其是在以特定视角为条件时。本文提出Hyper-CL，这是一种将超网络与对比学习相结合以计算条件化句子表示的高效方法。在我们提出的方法中，超网络负责将预先计算的条件嵌入转换为相应的投影层，从而使相同的句子嵌入能够根据不同条件被不同地投影。在两个具有代表性的条件化基准上的评估，即条件语义文本相似度和知识图谱补全，表明Hyper-CL能够灵活地对句子表示进行条件化，同时展示了其计算效率。我们还对该方法的内部工作机制进行了全面分析，从而更好地解释其机理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
