We present an easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models. The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. We use the original (monolingual) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model. Compared to other methods for training multilingual sentence embeddings, this approach has several advantages: It is easy to extend existing models with relatively few samples to new languages, it is easier to ensure desired properties for the vector space, and the hardware requirements for training is lower. We demonstrate the effectiveness of our approach for 10 languages from various language families. Code to extend sentence embeddings models to more than 400 languages is publicly available.

本文介绍了一种将现有的句子嵌入模型扩展到新语言的简便有效方法，训练基于将翻译后的句子映射到与原始句子相同的向量空间位置的思想，相较于其他多语言句子嵌入训练方法，具有扩展现有模型以增加新语言的简易性、保证向量空间所需属性的易操作性和较低的硬件要求等优势。代码已公开，可以用于将句子嵌入模型扩展到400多种语言。

使用知识蒸馏将单语句子嵌入多语言