We introduce a novel suite of state-of-the-art bilingual text embedding
models that are designed to support English and another target language. These
models are capable of processing lengthy text inputs with up to 8192 tokens,
making them highly versatile for a range of natural language processing tasks
such as text retrieval, clustering, and semantic textual similarity (STS)
calculations.
By focusing on bilingual models and introducing a unique multi-task learning
objective, we have significantly improved the model performance on STS tasks,
which outperforms the capabilities of existing multilingual models in both
target language understanding and cross-lingual evaluation tasks. Moreover, our
bilingual models are more efficient, requiring fewer parameters and less memory
due to their smaller vocabulary needs. Furthermore, we have expanded the
Massive Text Embedding Benchmark (MTEB) to include benchmarks for German and
Spanish embedding models. This integration aims to stimulate further research
and advancement in text embedding technologies for these languages.

该研究介绍了一套新颖的双语文本嵌入模型，能够处理长度长达 8192 个标记的文本输入，支持英语和目标语言，适用于文本检索、聚类和语义文本相似性计算等自然语言处理任务。通过专注于双语模型和引入独特的多任务学习目标，研究提高了 STS 任务模型性能，在目标语言理解和跨语言评估任务方面超过现有的多语言模型能力。此外，这些双语模型更高效，需要较少的参数和内存，因为它们具有较小的词汇需求。研究还扩展了大规模文本嵌入基准 (MTEB)，加入了德语和西班牙语嵌入模型的基准，旨在促进这些语言的文本嵌入技术的进一步研究和发展。