Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore, to use sentence embeddings in a particular domain, the model must be adapted to it in order to achieve good results. Usually, this is done by fine-tuning the entire sentence embedding model for the domain of interest. While this approach yields state-of-the-art results, all of the model's weights are updated during fine-tuning, making this method resource-intensive. Therefore, instead of fine-tuning entire sentence embedding models for each target domain individually, we propose to train lightweight adapters. These domain-specific adapters do not require fine-tuning all underlying sentence embedding model parameters. Instead, we only train a small number of additional parameters while keeping the weights of the underlying sentence embedding model fixed. Training domain-specific adapters allows always using the same base model and only exchanging the domain-specific adapters to adapt sentence embeddings to a specific domain. We show that using adapters for parameter-efficient domain adaptation of sentence embeddings yields competitive performance within 1% of a domain-adapted, entirely fine-tuned sentence embedding model while only training approximately 3.6% of the parameters.

翻译：句子嵌入使我们能够捕获短文本的语义相似性。大多数句子嵌入模型面向通用语义文本相似性任务进行训练。因此，要在特定领域使用句子嵌入，必须对模型进行领域自适应以获得良好效果。通常，这需要对整个句子嵌入模型进行微调。虽然该方法能取得最先进的结果，但微调过程中会更新模型所有参数，导致资源消耗巨大。为此，我们提出训练轻量级适配器，而非对每个目标领域单独微调整个句子嵌入模型。这些领域特定适配器无需调整底层句子嵌入模型的所有参数，而是仅训练少量附加参数，同时保持底层句子嵌入模型的权重固定。通过训练领域适配器，我们可始终使用相同的基座模型，仅需替换领域特定适配器即可将句子嵌入适应至特定领域。实验表明，采用适配器进行参数高效的句子嵌入领域自适应，其性能可达完全微调领域适配模型的1%以内，而仅需训练约3.6%的参数。