Hyper-CL: Conditioning Sentence Representations with Hypernetworks

arXiv:2403.09490v1 Announce Type: new
Abstract: 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 …

abstract art arxiv contributed cs.cl embeddings fine-grained frameworks introduction methodology paper perspectives representation representation learning semantics state type