Hyper-CL: Conditioning Sentence Representations with Hypernetworks
This addresses the need for more flexible and interpretable sentence representations in NLP, but it is incremental as it builds on existing contrastive learning methods.
The paper tackled the problem of whether state-of-the-art sentence embeddings can capture fine-grained semantics when conditioned on specific perspectives, and introduced Hyper-CL, which integrates hypernetworks with contrastive learning to compute conditioned sentence representations, showing effectiveness on benchmarks like conditional semantic text similarity and knowledge graph completion.
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.